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A STUDY ON PETER PRINCIPLE EFFECT IN
SOFTWARE DEVELOPMENT FIRMS OF SRI LANKA
Melanie Samaratunga
(9046)
Master of Business Administration in
Management of Technology
Department of Management of Technology
University of Moratuwa
Sri Lanka
November 2011
A STUDY ON PETER PRINCIPLE EFFECT IN
SOFTWARE DEVELOPMENT FIRMS OF SRI LANKA
Melanie Samaratunga
(9046)
Dissertation submitted in partial fulfillment of the requirements for the degree of
Master of Business Administration in Management of Technology
Department of Management of Technology
University of Moratuwa
Sri Lanka
November 2011
DECLARATION
I declare that this is my own work and this dissertation does not incorporate without
acknowledgement any material previously submitted for a Degree or Diploma in any
other University or institute of higher learning and to the best of my knowledge and
belief it does not contain any material previously published or written by another
person except where the acknowledgement is made in the text.
Also, I hereby grant to University of Moratuwa the non-exclusive right to reproduce
and distribute my dissertation, in whole or in part in print, electronic or other
medium. I retain the right to use this content in whole or part in future works (such as
articles or books).
……………………………. …………………………….
M. Samaratunga Date:
The above candidate has carried out research for the Masters dissertation under my
supervision.
……………………………. …………………………….
Prof. Vathsala Wickramasinghe Date
i
ABSTRACT
The Peter Principle — that individuals in an organization rise to their level of
incompetence —represents potential problems for all employees. Wondering if the
Peter Principle is prevalent in the software development firms of Sri Lanka, the
author revisited Dr. Laurence Peter’s study of 1969, The Peter Principle—Why
Things Always Go Wrong, which achieved best-seller status and soon became a part
of the lexicon of the business world.
A survey based study was carried out using respondents from selected software
development firms to identify if the effect exists. This study shows that the Peter
Principle, the universal phenomenon in which employees, around the world, are said
to rise to their level of incompetence, is ingrained in the software development firms
of Sri Lanka as well.
The behaviors embodied in the Peter Principle still have disrupting effects that occur
only too frequently in organizations. As a result, the Peter Principle cannot be
ignored. Its effects, however, can be remedied by improving the quality of
performance and rewards management practices, recruitment and selection practices
and by providing extensive training before employees reach their ultimate level of
incompetence.
ii
ACKNOWLEDGMENT
I would like to express my deepest appreciation to my supervisor, Prof. Vathsala
Wickramasinghe for her valuable support, ideas, and criticism during the springtime.
Her knowledge of the related literature, strong managerial perspective, and
willingness to exchange and shape ideas were crucial in the overall development of
this study. Your contributions had been of great value to the final result.
Additionally, I would like to convey my deep sense of appreciation for the
continuous help received from the team members of my organization, Virtusa
Corporation.
I would like to thank my family members for the contribution towards the success of
the research. They have sacrificed in many ways by allowing me to spend more time
on the study.
Further, the contribution of each and every respondent of the survey who extended
their support in the data collection process is very much appreciated.
Last but not least I thank all those whose names were, though not mentioned for their
help and encouragement in completing this research study.
iii
TABLE OF CONTENTS
DECLARATION.....................................................................................................................................i
ABSTRACT............................................................................................................................................ii
ACKNOWLEDGMENT......................................................................................................................iii
LIST OF TABLES................................................................................................................................vi
LIST OF FIGURES.............................................................................................................................vii
LIST OF ABBREVIATIONS............................................................................................................viii
CHAPTER 1 - INTRODUCTION........................................................................................................1
1.1 INTRODUCTION...............................................................................................................................11.2 RESEARCH PROBLEM......................................................................................................................31.3 RESEARCH OBJECTIVES..................................................................................................................81.4 SIGNIFICANCE OF THE STUDY........................................................................................................81.5 SCOPE OF THE RESEARCH STUDY..................................................................................................91.6 CHAPTER OUTLINE.......................................................................................................................10
CHAPTER 2 – LITERATURE REVIEW.........................................................................................11
2.1 INTRODUCTION.............................................................................................................................112.2 THE PETER PRINCIPLE..................................................................................................................11
2.2.1 Percussive Sublimation........................................................................................................132.2.2 Lateral Arabesque................................................................................................................132.2.3 Hierarchical Exfoliation.......................................................................................................132.2.4 Peter’s Inversion..................................................................................................................142.2.5 Paternal In Step....................................................................................................................152.2.6 Promotion by Pull and Push................................................................................................152.2.7 Final Placement Syndrome..................................................................................................152.2.8 Peter's Corollary..................................................................................................................17
2.3 PETER PRINCIPLE IN SOFTWARE DEVELOPMENT FIRMS..............................................................182.4 RESEARCHES CARRIED ON PETER PRINCIPLE EFFECT.................................................................192.5 SUMMARY....................................................................................................................................23
CHAPTER 3 - METHODOLOGY.....................................................................................................25
3.1 INTRODUCTION.............................................................................................................................253.2 CONCEPTUAL FRAMEWORK OF THE STUDY.................................................................................253.3 RESEARCH HYPOTHESES..............................................................................................................273.4 RESEARCH MODEL WITH HYPOTHESES........................................................................................273.5 OPERATIONALIZATION OF VARIABLES.........................................................................................29
3.5.1 The size of the company........................................................................................................293.5.2 The structure of the company...............................................................................................293.5.3 Quality of performance and rewards management practices..............................................303.5.4 Quality of Selection and recruitment practices....................................................................303.5.5 Human resource development practices..............................................................................30
3.6 UNIT OF ANALYSIS.......................................................................................................................353.6.1 Target Population.................................................................................................................35
iv
3.6.2 Data collection instrument...................................................................................................363.6.3 The method of data collection and analysis.........................................................................37
3.7. SUMMARY...................................................................................................................................37
CHAPTER 4 – DATA ANALYSIS AND DISCUSSION..................................................................38
4.1 INTRODUCTION............................................................................................................................384.2 CHARACTERISTICS OF THE SAMPLE...........................................................................................38
4.2.1 Characteristics of the organizations...............................................................................384.2.2 Characteristics of the respondents..................................................................................39
4.3 GOODNESS-OF-FIT MEASURES....................................................................................................434.3.1 Reliability and validity analysis - the existence of Peter Principle effect........................444.3.2 Reliability and validity analysis - the contributing factors for the existence of Peter Principle effect..............................................................................................................................47
4.4 DESCRIPTIVE STATISTICS.............................................................................................................504.5 CORRELATION ANALYSIS.............................................................................................................544.6 MULTIPLE REGRESSION ANALYSIS..............................................................................................584.7 SUMMARY....................................................................................................................................64
CHAPTER 5 – CONCLUSIONS AND RECCOMENDATIONS...................................................65
5.1 INTRODUCTION.............................................................................................................................655.2 CONCLUSIONS..............................................................................................................................655.3 RECOMMENDATIONS AND MANAGERIAL IMPLICATIONS.............................................................665.4 LIMITATIONS OF THE STUDY........................................................................................................715.5 DIRECTIONS FOR FUTURE RESEARCH..........................................................................................715.6 SUMMARY....................................................................................................................................73
REFERENCES.....................................................................................................................................74
Appendix A – Questionnaire..................................................................................................................78
Appendix B – SPSS Data Analysis Output............................................................................................83
v
LIST OF TABLES
Table 1.1: Summary of various researches carried out on software project failures ................................................. 3 Table 1.2: Summary of Standish chaos reports .......................................................................................................... 6 Table 3.1: Hypothesis statements ............................................................................................................................. 27 Table 3.2 : Operationalization of the identified variables ........................................................................................ 32 Table 3.3: Companies selected for the sample ......................................................................................................... 36 Table 4.1: Summary table for number of employees in organizations ..................................................................... 40 Table 4.2: Summary table for number of employees in different organization structures ....................................... 40 Table 4.3: Summary table for the age distribution of the respondents ..................................................................... 41 Table 4.4: Summary table for the gender distribution of the respondents ............................................................... 41 Table 4.5: Summary table for the marital status of the respondents ........................................................................ 41 Table 4.6: Summary table for the education level of the respondents ..................................................................... 42 Table 4.7: Summary table for the designation level of the respondents .................................................................. 43 Table 4.8: Summary table for the years of experience in IT of the respondents ...................................................... 43 Table 4.9: Summary table for the years of experience in the current company of the respondents ......................... 44 Table 4.10: Summary table for the years of experience in the current post of the respondents ............................... 44 Table 4.11: Reliability analysis for the factors that determine the existence of Peter Principle effect .................... 45 Table 4.12: Principle component analysis for the factors that determine the existence of Peter Principle effect . . . 46 Table 4.13: Rotated component matrix for the factors that determine the existence of Peter Principle effect ........ 46 Table 4.14: Reliability analysis results for quality of performance and rewards management practices related variables .................................................................................................................................................................... 48 Table 4.15: Principle component analysis results for quality of performance and rewards management practices related variables ........................................................................................................................................................ 48 Table 4.16: Factor loadings for quality of performance and rewards management practices related variables ...... 49 Table 4.17: Reliability analysis results for quality of recruitment management practices related variables ........... 49 Table 4.18: Principle component analysis results for quality of recruitment management practices related variables .................................................................................................................................................................... 50 Table 4.19: Factor loadings for quality of recruitment management practices related variables ............................. 50 Table 4.20: Reliability analysis results for quality of human resource development practices related variables....50Table 4.21: Principle component analysis results for quality of human resource development practices related variables .................................................................................................................................................................... 51 Table 4.22: Factor loadings for quality of human resource development practices related variables ..................... 51 Table 4.23: Descriptive statistics for the factors that explain the existence of Peter Principle effect ...................... 52 Table 4.24: Descriptive statistics for the factors that explain the quality of performance and rewards management practices .................................................................................................................................................................... 53 Table 4.25: Descriptive statistics for the factors that explain the quality of selection and recruitment practices . . . 54 Table 4.26: Descriptive statistics for the factors that explain the quality of human resource development practices .................................................................................................................................................................................. 54 Table 4.27: Correlation analysis results ................................................................................................................... 56 Table 4.28: Model summary for the existence of Peter Principle related behavior patterns and the contributing factors ....................................................................................................................................................................... 59 Table 4.29: ANOVA table for existence of Peter Principle related behavior patterns and the contributing factors60 Table 4.30: Coefficients for existence of Peter Principle related behavior patterns and the contributing factors ... 60 Table 4.31: Model Summary for the existence of incompetent employees in the higher levels of the hierarchy and the contributing factors ............................................................................................................................................. 61 Table 4.32: ANOVA table for the existence of incompetent employees in the higher levels of the hierarchy and the contributing factors ............................................................................................................................................. 62 Table 4.33: Coefficients for the existence of incompetent employees in the higher levels of the hierarchy and the contributing factors ................................................................................................................................................... 62 Table 4.34: Group Statistics for structure of the company ....................................................................................... 63
vi
LIST OF FIGURES
Figure 3.1: Conceptual Model .................................................................................................................................. 26 Figure 3.2: Conceptual Model with Hypotheses ...................................................................................................... 28 Figure 4.1: Revised Conceptual Model....................................................................................................................47
vii
LIST OF ABBREVIATIONS
IT – Information Technology
ICT – Information Communication Technology
IFS – Industrial and Financial Systems
BPO – Business Process Outsourcing
RPO – Recruitment Process Outsourcing
SPSS – Statistical Package for the Social Sciences
viii
CHAPTER 1 - INTRODUCTION
1.1 Introduction
Sri Lanka is emerging as a global IT-BPO destination of choice in a number of key
focus domain areas. It is ranked among the Top 50 Global Outsourcing destinations
by A.T. Kearney according to Sri Lanka Business Portal - Trade Information (2011).
In addition, Sri Lanka has emerged as the most preferred ICT/BPO hub in the Asian
region and is the destination renowned for Best-Of-Breed in Global Market.
Sri Lanka acts as an off-shore development center for several Fortune 500 companies
from the USA, Ireland, UK, Australia, etc and joint venture development center
companies from Sweden, Norway, USA, Japan etc. Some business entities that have
set-up their operations in the island include: HSBC, Industrial & Financial Systems
(IFS), Amba Research, RR Donnelley, Quattro, Virtusa, eCollege, Eurocenter,
Aepona, Millennium Information Technology and Innodata Isogen etc. At present
there are over 300 IT and BPO companies that operate in Sri Lanka, mostly small
and medium companies and a few large global players.
According to Sri Lanka Business Portal - Trade Information (2011), Sri Lanka offers
a rapidly growing, highly adaptable, innovative and loyal workforce. Currently, over
50,000 are employed in the IT and BPO industry in Colombo and the workforce is
growing at over 20% year-on-year. The workforce is stable with very low attrition
rates ranging from 10-15%.
The Software services sector focuses on telecommunication, banking, financial
services and insurance and software testing. Earnings from exports of IT-BPO sector
have shown a steady upward trend during the past decade, and annual exports of the
ICT sector for the last three years recorded as US $ 213 million in 2007, US $ 256
million in 2008 and US $ 271 million in 2009. The industry has set a target of $ 2bn
in export revenue from IT-BPO sector by 2012.
1
Sri Lankan IT industry shows good potential, but in the long term for any industry to
succeed it is highly necessary that a competent workforce is available. For this talent
should be managed effectively.
Talent management is very important for a number of reasons. It is about people and,
without people an organization would not exist. Employers should understand where
the strengths and weaknesses of their employees lie, in order to ensure they are
correctly deployed across the business. Identifying where the promising talent lies,
allows a company to plan ahead and nurture staff, so their personal development is
serving organizational development benefitting both parties.
One of the challenges of talent management in business is, knowing whether a
seemingly talented individual will thrive if promoted to a new task or area. Once
assigned and moved into a talent pool, these individuals must also be managed
properly, in terms of their own expectations and those of the employer.
Peter and Hull (1969), in their book, The Peter Principle state that, “in a hierarchy,
every employee tends to rise to his level of incompetence”, which means that in a
hierarchy, members are promoted so long as they work competently. Eventually they
are promoted to a position at which they are no longer competent i.e. their level of
incompetence and there they remain, being unable to earn further promotions.
To further elaborate on the Peter Principle theory, it is a concept that in
organizations, new employees typically start in the lower ranks, but when they prove
to be competent in their current designation, they get promoted to a higher rank,
generally management. This process of climbing up the hierarchical ladder can go on
indefinitely, until the employee reaches a position where he or she is no longer
competent. At that moment, the process typically stops, since the established rules of
bureaucracies make it very difficult to demote someone to a lower rank, even if that
person would be a much better fit and happier in a lower rank. The net result of this
principle is that, most of the higher levels of a bureaucracy will be filled by
incompetent people, who got there because they were quite good at doing different
and usually, but not always, easier work than the work they are expected to perform
at present. For example if you're a proficient and effective software developer, you're
2
most likely demonstrating high competence in your job right now. As a result of your
performance, your valuable contribution results in a promotion to a management
position. In this new position, you now do few of the original tasks which gained you
acclaim. In fact, little of your current job remains enjoyable, therefore your heart is
no longer in your work, and it shows. Given this, promotions stop, and there you
stay, until you retire or your company goes under due to mismanagement.
According to this principle, work is accomplished by those employees who have not
reached their level of incompetence. Thus we can see why organizations still
function even as Peter Principled employees accept promotions. Dr. Peter (1969)
provides an insightful analysis of why so many positions in so many organizations
seem to be populated by employees who seem incompetent. This concept is likely to
be ignored by most senior managers, since to admit one's organization is suffering
from this bureaucratic dilemma is admission that, people have been improperly
promoted. This, in turn, suggests that senior management might have attained their
own level of incompetence, and the problem is easily ignored, in case it become
suggested that senior management be more closely examined for their incompetence.
Once a company forms a culture of incompetence, only the incompetent staff will
remain, and the competent ones will eventually get frustrated and leave. As a result
the organization’s growth will hinder as they have incompetent employees at many
levels.
1.2 Research Problem
According to the article Facts and Figures – Why Technology Projects Fail (2011), a
number of studies have been completed that look into the success and failure rates of
software projects. These studies indicate that serious problems exist across the
industry as a whole. Table 1.1 summarizes some recent reports.
Table 1.1: Summary of various researches carried out on software project failures
Source Type of Survey Date Result
Geneca Interview based study
of software projects
2010-
2011
Interviews with 600 people closely
involved in software development projects
finds that even at the start of a project
3
many people expect their projects to fail
“Fuzzy business objectives, out-
of-sync stakeholders, and
excessive rework” mean that 75%
of project participants lack
confidence that their projects will
succeed.
A truly stunning 78% of
respondents reported that the
“Business is usually or always out
of sync with project
requirements”
KPMG (New
Zealand)
Survey of 100
businesses across a
broad cross-section of
industries
Dec 2010 KPMG survey of Project Management
practices in New Zealand finds some truly
startling results:
Survey shows an incredible 70%
of organizations have suffered at
least one project failure in the
prior 12 months
50% of respondents also indicated
that their project failed to
consistently achieve what they set
out to achieve
IBM Survey of 1,500
change management
executives
Oct 2008 IBM survey in the success / failure rates of
“change” projects finds:
Only 40% of projects met
schedule, budget and quality
goals
Best organizations are 10 times
more successful than worst
organizations
Biggest barriers to success listed
as people factors: Changing
mindsets and attitudes - 58%.
Corporate culture - 49%. Lack of
senior management support -
32%.
4
Underestimation of complexity
listed as a factor in 35% of
projects
United States
Government
Accountability
Office
Review of federally
funded technology
projects
31 Jul
2008
Study finds 413 of 840 (49%) federally
funded IT projects are either poorly
planned, poorly performing or both.
Information
Systems Audit
and Control
Association
(ISACA)
400 respondents May
2008
Key findings
43% of organizations have
suffered a recent project failure
At a typical enterprise 20% of
technology investments are not
fully realized
Guardian
Newspaper (UK
)
Investigation into
government waste in
the UK since year
2000
5 Jan
2008
Study of government projects
reveals $4billion in wasted efforts
as a result of failed projects
“Only 30% of our projects and
programs are successful” -Joe
Harley, programme and systems
delivery officer at the Department
for Work and Pensions
Dr Dobbs
Journal
586 respondents to
email survey (Dr
Dobbs subscriber list)
Aug
2007
70% of respondents had been
involved in a project they knew
would fail right from the start
Success rates for Agile projects
72%, success rate for traditional
approaches 63%
KPMG - Global
IT Project
Management
Survey
Survey of 600
organizations
globally
2005 In just a 12 month period 49% of
organizations had suffered a
recent project failure
In the same period only 2% of
organizations reported that all of
their projects achieved the desired
benefits
86% of organizations reported a
shortfall of at least 25% of
targeted benefits across their
5
portfolio of projects
Many organizations fail to
measure benefits so they are
unaware of their true status in
terms of benefits realization
The following research findings were listed in the article, Software Project Failure
Costs Billions (2008).
Standish Chaos Reports: Standish reports define success as projects on budget, of
cost, and with expected functionality. Standish findings by year, are shown in table
1.2.
Table 1.2: Summary of Standish chaos reports1994 1996 1998 2000 2002 2004 2009
Succeeded 16% 27% 26% 28% 34% 29% 32%
Failed 31% 40% 28% 23% 15% 18% 24%
Challenged 53% 33% 46% 49% 51% 53% 44%
Mercer Consulting: When the true costs are added up, as many as 80% of
technology projects actually cost more than they return. It is not done intentionally
but the costs are always underestimated and the benefits are always overestimated.
Oxford University: Regarding IT project success reported the following figures.
Successful: 16%
Challenged: 74%
Abandoned: 10%
British Computer Society: In 2004, the UK public sector spent an estimated 12.4
bn. on software and the overall amount spent on IT was about 22.6 Billion British
Pounds. From those projects the success rate was 16%.
6
National Institute of Standards and Technology (NIST): According to NIST
Software defects cost nearly $60 billion, annually
80% of development costs involve identifying and correcting defects
Tata Consultancy: In 2007 Tata consultancy reported that
62% of organizations experienced IT projects that failed to meet their
schedules
49% suffered from budget overruns
47% had higher-than-expected maintenance costs
41% failed to deliver the expected business value and ROI
33% file to perform against expectations
From Bob Lawhorn presentation on software failure (March 2010):
Poorly defined applications (miscommunication between business and IT)
contribute to a 66% project failure rate, costing U.S. businesses at least $30
billion every year (Forrester Research)
60% – 80% of project failures can be attributed directly to poor requirements
gathering, analysis, and management (Meta Group)
50% are rolled back out of production (Gartner)
40% of problems are found by end users (Gartner)
25% – 40% of all spending on projects is wasted as a result of re-work
(Carnegie Mellon)
Up to 80% of budgets are consumed fixing self-inflicted problems (Dynamic
Markets Limited 2007 Study)
Considering all of the research findings above, it is clear that the software project
failure rates are high, globally. Most of the problems have occurred due to poor
quality products, lack of proper leadership, unrealistic estimations etc. Hence it is
reasonable to assume that these problems might have occurred due to incompetency
of the employees i.e. poor quality code could have been a result of software
developed by unskilled software developers, inability to meet deadlines would have
been a result of estimates created by incompetent project leads etc. As mentioned
7
earlier incompetence in an organization’s hierarchy denotes the existence of Peter
Principle effect.
The risks of over promoting and under developing employees lead to many problems
in an organization. One such problem is that, it could lead to the perception that a
culture of incompetence is being fostered, resulting in your competent staff
becoming frustrated and leaving the organization. Problems as such eventually lead
to the downfall of the company. Hence the Peter Principle effect needs to be avoided
in organizations for them to grow and succeed. This research was designed to
identify if Sri Lankan software development firms are victims of this effect and to
develop strategies to avoid it.
1.3 Research Objectives
The research objectives are as follows.
Objective 1:
To identify if the Peter Principle effect exists in Software development firms of Sri Lanka
Objective 2:
To identify and analyze the impact of the determining factors that associate with the existence of Peter Principle effect
Objective 3:
To propose recommendations to help software firms in Sri Lanka to avoid the Peter Principle effect
1.4 Significance of the Study
A decisive success factor for software producers is the quality of their software.
Software systems must meet steadily rising demands regarding stability,
performance, usability and maintainability. Economic indicators such as
development time and costs need to be considered as well. All these together define
high quality software which will define the success of the software development
organization.
8
The human resources are the most important assets of any organization. The success
or failure of an organization is largely dependent on the caliber of the people
working therein. Without positive and creative contributions from people,
organizations cannot progress and prosper. In order to achieve the goals of an
organization, it should have a highly competent workforce and the right people
should be employed in the right jobs. The absence of the above and the presence of
incompetent employees in the wrong jobs denote the existence of Peter Principle
effect. As mentioned earlier, once a company forms a culture of incompetence, only
the incompetent staff will remain, and the competent ones will get frustrated and
leave the company making the company’s growth hinder as they have incompetent
employees at many levels. Hence it is important to avoid this malady to ensure that
the software industry in Sri Lanka grows to its maximum potential.
This study intended to identify, if the Peter Principle effect exists in software
development organizations of Sri Lanka and to provide remedies to avoid it. This
will help software development organizations to properly manage talent and identify
where the promising talent lies, allowing them to plan ahead and nurture staff, so
their personal development is serving organizational development benefiting both
parties.
1.5 Scope of the Research Study
The main focus area of the study was to identify if the Peter Principle effect exists in
software development organizations of Sri Lanka and to recommend a set of
solutions to avoid the effect.
The scope of the research will be limited to software development organizations
within Sri Lanka. The selected software organizations use either tall or flat
organizational structures. The sample of respondents will be selected at random from
the selected organizations. The software development firms selected will be small,
medium or large scale.
The research methodology will be focusing on collecting information from the
employees through online questionnaires. The questionnaires will be targeted
9
towards Software Engineers, Business Systems Analysts, Quality Assurance
Engineers, Team Leads, Project Managers and Architects who are in the middle level
management or the lower levels of the hierarchy so that the sample selected will have
fewer employees who have reached their highest level of incompetence.
1.6 Chapter Outline
The outline of the report is as follows.
Chapter 2, discusses the previous researches carried out on Peter Principle effect by various researchers.
Chapter 3, discusses the design of the study, developing the conceptual model based on the literature that was reviewed in chapter 2, development of hypotheses for the purpose of checking the validity of the relationships of among constructs, data collection methods and operationalization of measurements.
Chapter 4, discusses the data analysis and hypotheses testing carried out. It includes primary and secondary data analysis, reliability and validity testing of the data set, descriptive data analysis, correlation analysis and regression analysis.
Chapter 5, concludes the entire study and discusses the findings of the study and recommends suggestions, guidelines and strategies to avoid the Peter Principle in Sri Lankan IT Firms. The chapter will also highlight limitations of the study and future research opportunities.
10
CHAPTER 2 – LITERATURE REVIEW
2.1 Introduction
This chapter contains a comprehensive analysis on the researches carried out
previously. It is important to understand the viewpoints of the earlier studies to get a
thorough understanding on the research area and ensure that the research covers all
the required areas in the formation of a comprehensive view of the total scenario.
Thus, the literature review would create the foundation of the total study based on the
previous literature and the discussions which have taken place on the topic.
2.2 The Peter Principle
“In a hierarchy every employee tends to rise to his level of incompetence”
(Peter and Hull 1969, p.25)
The above quote was taken from Dr.Laurence J.Peter and Raymond Hull’s
bestselling book: “The Peter Principle – Why things always go wrong?” This sets the
outline for this research.
The Peter Principle theory was introduced by Dr. Laurence J. Peter in the year 1969.
He was a sociologist, who taught at the University of British Columbia before
becoming a professor of education at the University of Southern California (Taylor,
1969). He was an expert in the area of hierarchical incompetence and wrote a couple
of books about this controversial topic. His first book, “The Peter Principle - Why
things always go wrong?” introduced the Peter Principle to the world. He claimed
that in a hierarchy, every employee tends to rise to his level of incompetence (Peter,
1969, p. 25). Additionally, his view was that one will advance to his highest level of
competence and consequently get promoted to a position where he will be
incompetent. The book contains many real-world examples and thought-provoking
explanations of human behavior, including the fact that “Every organization
11
contained a number of persons who could not do their jobs, and that occupational
incompetence is everywhere” (Peter, 1969, p. 20).
It is deciphered in a multifactor framework and is based on a study in which, data in
the form of hundreds of case histories were collected through observing overt
behavior and avoiding introspection or inferences. Peter and Hull(1969) concluded
that an employee’s process of climbing up the hierarchical ladder in an organization
can go on indefinitely until the employee reaches a position where he or she is no
longer competent and is, thus, regarded as incompetent. It states that in time, every
post tends to be occupied by an employee who is incompetent to carry out their
duties and adds that, work is accomplished by those employees who have not yet
reached their level of incompetence.
Why a research is still needed about the Peter Principle, which was developed over
40 years ago? Schapp and Ogulink(2009), in their recent research paper, shows that
the Peter Principle, is still prevalent today and little regarding its presence has
changed since 1969. 73% of the participants in their study had said that they have
seen a Peter Principle situation happen within the last five years.
The ability of an employee is determined not by outsiders, but by his or her superior
in the hierarchy. At that point if the employee has reached his level of incompetence,
the upward process usually stops since the recognized rules of organizations make it
very difficult to demote someone, even if that person would fit in much better in a
lower job. The end result is that most of the higher levels of an organization will be
filled by inept people. For e.g. managers, who got there because they had previously
shown competence in doing a task, different than the new one they are expected to
do.
Essentially, Peter said that as employees move upward through the chain of
command, they do worse, as managers, than they did before having been promoted.
And this phenomenon is not limited in scope. According to Peter and Hull(1972, p.
24) “Sooner or later, this could happen to every employee in every hierarchy
business, industry, trade-unions, politics, government, armed forces, religion, and
education”.
12
Peter and Hull (1969) points out several symptoms to identify the existence of Peter
Principle effect in organizations. These come in the form of behavioral patterns of
incompetent employees or techniques used by organizations to deal with employees,
who have reached their level of incompetence. Some of the important symptoms are
as follows:
2.2.1 Percussive Sublimation
According to Peter and Hull (1969, p.37) this is a pseudo-promotion technique where
an incompetent employee is promoted to a higher position which brings on no new
responsibility, but unclogs the rest of the hierarchy. This is commonly known as
kicking a man upstairs. The objective of Percussive Sublimation usually is to deceive
the outside world. It camouflages the flaws in the employer’s promotion policy,
supports staff morale, and maintains the hierarchy in lieu of firing the incompetent
person which might result in him getting another job with a competitor where,
despite his incompetence, his knowledge could be dangerous.
2.2.2 Lateral Arabesque
Lateral Arabesque is another pseudo-promotion. Without being raised in rank,
sometimes without even a pay raise, the incompetent employee is given a new and
longer title and is moved to an office in a remote part of the building, states Peter and
Hull (1969, p.39). This is similar to Percussive Sublimation where the main objective
is to unclog the hierarchy by removing the incompetent employee, so that the
workflow can run smoothly.
2.2.3 Hierarchical Exfoliation
Peter and Hull (1969, p.45) states that, in most hierarchies, super-competence is
more objectionable than incompetence. Ordinary competence is no cause for
dismissal: it is simply a bar for promotion. On the other hand super-competence
often leads to dismissal as it disrupts the hierarchy. The super-competents, who seem
13
to know everything and do everything well at all levels in anticipation of always
moving up, are more likely to be fired because they disrupt the hierarchy. In Peter's
view, these people attract too much attention to themselves, worrying others in the
organization so much, that they disrupt the super-competent's climb to the top. Hence
super-competent employees often get stuck in their ranks or get dismissed to
preserve the hierarchy.
This phenomenon is often related with negative selection. Negative selection is a
political process that occurs especially in rigid hierarchies, most notably
dictatorships. The person on the top of the hierarchy, wishing to remain in power
forever, chooses his associates with the prime criterion of incompetence – they must
not be competent enough to remove him from power. The associates do the same
with those below them in the hierarchy, and the hierarchy is progressively filled with
more and more incompetent people.
If the dictator sees that he is threatened nonetheless, he will remove those that
threaten him from their positions and emptied positions in the hierarchy are normally
filled with people from those who were less competent than their previous masters.
So, over the course of time, the hierarchy becomes less and less effective. As this
happens relatively often, once the dictator dies, or is removed by some external
influence, what remains is a grossly ineffective hierarchy.
2.2.4 Peter’s Inversion
Peter’s Inversion also known as the professional automaton, is when there are
employees who have little or no capacity for independent judgment but always obey
and never decide. These are the kind of employees who show obsessive concern with
filling out forms correctly permitting no deviations from established routine. To a
person who follows the professional automaton, it is clear that means are more
important than ends; the paperwork is more important than the purpose for which it
was originally designed. He no longer sees himself as existing to serve the public; he
sees the public as the raw material that serves to maintain him, the forms, the rituals,
14
and the hierarchy. Unfortunately for the public, the automaton appears to be
competent from the hierarchy’s point of view. As a result he remains eligible for
promotion until by some mischance he is elevated into a position where he absolutely
has to make a decision. It is at that point that he reaches his level of incompetence,
wrote Peter and Hull (1969, p.41). These people are often managed by incompetent
managers who care about sycophancy, courtesy towards bosses, etc. more than one's
internal efficiency.
2.2.5 Paternal In Step
Paternal In Step is when a family member is promoted several steps above his or her
level of incompetence. According to Peter and Hull (1969, 49-50), this could take
place in two ways: an existing employee may be dismissed or removed by lateral
arabesque or percussive sublimation, to make a place for the in stepper, or a new
position with an impressive title is created for the in-stepper . These techniques may
cause considerable ill-feeling towards the new appointee.
2.2.6 Promotion by Pull and Push
Peter and Hull (1969) suggests two main means by which a person can affect
promotion rate, Pull and Push. Pull is an employee's relationship - by blood,
marriage or acquaintance with a person above the employee in the hierarchy. Push on
the other hand is sometimes manifested by an abnormal interest in study, vocational
training and self-improvement. In small hierarchies, Push may have a marginal effect
in accelerating promotion; in larger hierarchies the effect is minimal. Pull, is
therefore likely to be more effective than push. Peter & Hull (1969, p.63) states
“Never stand when you can sit; never walk when you can ride, never Push when you
can Pull”.
2.2.7 Final Placement Syndrome
According to Peter and Hull (1969, p.108) when an employee reaches his level of
incompetence, he can no longer do any useful work. This was termed as Final
Placement Syndrome by Dr.Peter(1969). He further discusses some symptoms to
identify employees who have reached this state. It is found out that such employees
15
are overly stressed, mentally disturbed and frequently sick (Peter and Hull, 1969,
109-115).
Peter and Hull (1969, 116-127) lists some areas of behavior which identify those
who have reached their highest level of incompetence.
Abnormal Tabulology: This is an important area of hierarchiology. A competent
employee normally keeps on his desk just the books, papers, and apparatus that he
needs for his work. After final placement, an employee is likely to adopt some
unusual and highly significant arrangement of his desk.
Papyromania: This manifestation of final placement causes the employee to clutter
his desk with piles of never used papers and books. Consciously or unconsciously, he
thus tries to look busy and mask his incompetence by giving the impression that he
has too much to do than any human could accomplish.
Fileophilia: Here we see a mania for the precise arrangement and classification of
papers, usually combined with a morbid fear of losing any document. By keeping
himself busy rearranging and re-examining bygone business, the fileophiliac
prevents other people-or himself-from realizing that he is accomplishing little or
nothing of current importance.
Self-pity: One excellent indication of final placement is the telling of chronic hard-
luck stories. It is always the fault of someone outside and beyond the pitier’s control
that makes them incompetent. This self-pity is usually combined with a strong
tendency to reminisce about "the good old days," when the complainant was working
at a lower rank, a level of competence.
Cachinatory Inertia: The habit of telling jokes instead of getting on with business.
Side-Issue Specialization: a commonplace substitute for competence characterized
by the motto: "Look after the molehills and the mountains will look after
themselves."
16
Substitution: Once an employee has reached his level of incompetence, he must
engage in one or more substitutions to keep sane and happy. Otherwise he would
have to face the Sordid Truth, that he is unfit and incompetent to do the job.
Buck passing: Passing the buck is another symptom. It is the act of attributing
another person or group with responsibility for one's own actions
2.2.8 Peter's Corollary
Peter's Corollary states that "in time, every post tends to be occupied by an employee
who is incompetent to carry out their duties" and adds that "work is accomplished by
those employees who have not yet reached their level of incompetence"(Peter and
Hull 1969,p.27). "Managing upward" is the concept of a subordinate finding ways to
subtly manage superiors in order to limit the damage that they end up doing.
Peter (1985, p. 28) wrote in “Why Things Go Wrong or the Peter Principle
Revisited”, that: “I named it The Peter Principle, because it described a
generalization or a tendency and not something inevitable”. The typical
organizational systems encourage individuals to climb to their levels of
incompetence. If you are able to do your job efficiently and with ease, you will be
told that you need to take up more challenges and you will be moved up.
Nevertheless the problem is that when you find something you can’t do very well,
that is where you stay, inept in the job, frustrating your co-workers, and harming the
effectiveness of the organization.
However Peter (1972, p. 35) states that he first wrote about the Peter Principle, he
assumed it applied to all or at least most professions, but he could not be certain.
Although it was impossible for him to study every organization that existed in the
world, the ones he investigated conformed to the principle. Hence further research is
needed to identify if the Peter Principle is prevalent in the IT industry. As IT is a
fairly new industry, minimal research has been done to identify if the Peter Principle
exists in its context.
17
2.3 Peter Principle in Software Development Firms
A software company is made up of employees who either follow the management
track or the technical track. Hence the Peter Principle could affect a software
company mainly in two different ways. The first scenario is when a technical person
gets promoted to a managerial position. For e.g. programmers make up the majority
of the entry level roles in software companies. These people have highly developed
technical skills. A successful programmer will usually, after an appropriate time, be
offered a promotion. That's what tends to happen in a hierarchy, and the prospect of a
career path is what attracts many people to entry-level jobs in these organizations.
Once he becomes a Senior Engineer, he'll be dealing with more challenging projects.
But he'll still be using the same skills, just at a more advanced level. Then, the time
comes for him to be promoted again, and this time he will be made a manager, who
is in charge of a team of programmers. This is where things can start to go wrong.
While his knowledge of the company, its products and its clients mean that he's well
placed to be managing a department, he may not have any of the soft skills needed to
handle people, or liaise with other teams and senior management. His technical
expertise is no longer useful to him. As a result, his performance in this new role
may be poor. If he can't improve his soft skills, he'll never be promoted again. But
because people are rarely demoted in a hierarchy, he'll remain at that level – his level
of "incompetence" – doing a bad job. Not only will this make him unhappy, but the
organization will suffer too. Taken to its extreme, many of the roles in the upper part
of a hierarchical organization may be occupied by people who are not particularly
good at their jobs.
Cline, Lomow and Girou(1998,p.301) states that when exit interviews are reviewed
for technical workers, two troubling facts are noted:
1. Technical workers with the highest appraisal scores tend to leave in the largest
numbers.
2. The most common reason cited for leaving a company is “I don’t like working for
bad management.”
18
The second scenario is when incompetency occurs in technical positions. For e.g.
there may be employees with average technical skills. Once these employees are
promoted to a higher level, say from software engineer to senior software engineer,
they may lack the necessary competence to cope with the new responsibilities
assigned to them. For e.g. a software engineer may not be required to do software
designs, but once he becomes a senior software engineer, his new responsibilities
may require him to do software designing as well. At this point, if the employee is
not good at the task, he may end up designing software that are of very low quality.
This may lead to the Software Peter Principle. According to Cline, Lomow and
Girou(1998,p.47) ,the Software Peter Principle is in operation when unwise
developers improve and generalize the software until they themselves can no longer
understand it, then the project slowly dies. The Software Peter Principle can ruin
projects. The insidious thing about it is that it's a silent killer. By the time the
symptoms are visible, the problem will have spread throughout every line of code in
the project. This principle has been derived from the Peter Principle.
There may be various other instances where incompetence of employees can occur.
But in this research we will focus more on the above mentioned two areas of
incompetence.
2.4 Researches Carried on Peter Principle Effect
A number of researches has been carried out to identify the causes of Peter Principle
Effect and to find a solution to the problem. They are listed down below
chronologically, to highlight development of the thinking of various writers about
this controversial topic from 1969 to date.
Where and by whom, the, know-how of an employee is determined? According to
Peter (1969), employee competence is determined not by outsiders, but by the
employee’s superior in the hierarchy. If the superior is still at a level of competence,
that person may evaluate subordinates in terms of the performance of useful work i.e.
the evaluation of actual output. On the other hand, if the superior has reached a level
of incompetence, that person will probably rate subordinates, in terms of institutional
19
values i.e. the superior will see competence as the actions that support the rules,
rituals, and forms of the organization as they are, as opposed to how they should be.
Peter (1969) concluded from his studies that what appear to be exceptions are not
exceptions at all, because even though employees want to be productive, the Peter
Principle still applies to all employees in, all hierarchies.
Minter (1972) mentioned, as part of the “Peter Principle in Training”, that
individuals who have been in charge of planning and developing training programs
have historically had little or no formal training to prepare them for such a position.
Thus, they usually lack training in educational principles, psychology of learning,
communication and instruction, and in methods of testing and evaluation. As a result,
individuals who have to assume responsibility for planning and training, often learn
by trial-and-error at the expense of both the trainees and the organization.
The author of “CPAs Meet the Peter Principle” (1988) stated and seemed to support
Peter (1969) in that: Everything Dr. Peter predicted in The Peter Principle is coming
home to roost in the field where stability is such a virtue, that nobody ever thought it
would happen. Employees, who are continually promoted because the next slot is
vacant, not necessarily because they are qualified, will eventually be promoted to
their levels of incompetence.
Koontz and Weihrich (1990, p.236) pointed out that errors in the selection process
can lead to actualization of Peter Principle.
Odiorne (1991) pointed out, even though not mentioning anything about the Peter
Principle per se, that people have more talent and intelligence than we often assume.
This researcher also said that employees should be taught the skills and tasks in order
to be knowledgeable, because ongoing training can prevent competence from eroding
and becoming obsolete.
Gately (1996), found that employees can avoid the Peter Principle as long as
employees are judged on technical merit and accomplishment, and that promotions
go to the technically proficient and verbally expressive employees, while the less
technically proficient and verbally expressive wait their turn.
20
Anderson, Dubinsky, and Mehta (1999) wrote that, sales performance is determined
by how well the sales manager can motivate, lead, and control sales-force operations.
But, whether viewed from the perspective of salespeople, customers, sales managers
themselves, or top management, there is concern that sales organizations are not
performing as desired. Their findings support the contention that, sales managers
may well be marketing’s, best example of the Peter Principle: They too have arrived
at their level of organizational incompetence.
Faria (2000) mentioned concern about the role of promotions to manager and its
impacts on the firm behavior, assuming an internal labor market structure. Faria
further stated that a shortcoming of this process is that, people can be placed in
important jobs for which they are ill-qualified. That is, the Peter Principle can be an
outcome of this process, where people are promoted out of jobs for which they are
overqualified until they reach ones, where the job demands are suited to maximum of
individual ability levels. Namely, they are on the edge of their competence, so they
cannot achieve anything more than what they had already achieved.
Fairburn and Malcolmson (2000) have put forward as a basis of argument, that if a
firm provides incentives by promoting those who have performed well in a given job,
it may simply transfer them to another job to which they are not well suited—that is
a mild version of the Peter Principle.
Lazear (2004, p.159), in his theoretical model, which was a review of Peter’s work,
concluded the following from direct Peter Principle research: Workers who are
promoted, receive this treatment because they are observed to have exceeded some
standard. Part of the observation is based on lasting ability, but part is based on
transitory components that may reflect measurement difficulties, short-term luck, or
skills that are job specific. As a result, there is regression to the mean, creating a
Peter Principle effect.Workers who are promoted do not appear to be as able, as they
were before the promotion.
Lazear (2004, p. 159) further deduced the following: Firms take this phenomenon
into account in setting up their promotion rule. Under general conditions, when fewer
than 50% of the workers are better suited to the high level job, the firm adjusts the
21
promotions standard upward to compensate for the regression to the mean. The
amount of the adjustment depends on the tightness of the error distribution. When the
pre-promotion error has high dispersion, promotion standards are inflated by more
than they are when the error dispersion is low.
And finally, Lazear (2004) deduced that all workers who remain at a given level will
be incompetent in that they are neither as good as the average worker coming into the
job nor are they as good as they were in their previous job relative to their
comparison set. Lazear (2004, p. 148) also stated that: “The regression-to-the-mean
phenomenon implies that movie sequels are lower quality than the original films on
which they are based and that excellent restaurant meals are followed by ones that
are closer to mediocre”
King (2004), in the same year, speculated that persons in bureaucratic institutions are
promoted until they reach the level of their incompetence and remain there until they
quit, retire, or rarely, are fired. Furthermore, King stated that this phenomenon does
not occur only in governmental institutions. In many publicly held companies in
corporate America, the exercise of less oversight than is exerted in governmental
agencies lends itself to layers of bureaucracy and incompetence. To a lesser degree,
small businesses are also plagued by this.
Fetzer (2006) mentioned that as people climb up the organizational ladder, they reach
a level within the organization in which, they cannot perform competently, which
leads to a dead-wood supervisor/manager/executive whose position and its duties are
too much for this person to handle well.
In 2007, Chapman affirmed that: For every job in the world, there is someone who
cannot do it.
Newman (2008) cited an affable but invasive regional manager (i.e., M. Scott) as the
type of person who rises just above his abilities and then plateaus at a level of
incompetence. A. Donovan, professor of business ethics at Dartmouth’s Tuck
Business School, posited that: Ninety percent of the population deals with the M.
Scott’s in their daily lives (Newman, 2008, p. 6).
22
According to Schapp & Ogulink(2009, p.2), in their research, Peter Principle, the
phenomenon in which employees around the world, are said to rise to their level of
incompetence, is still prevalent today and that little regarding its use has changed
since 1969. Seventy-three percent of the participants in their study said that, they
have seen a Peter Principle situation happen within the last five years.
Pluchino, Rapisarda and Garofalo(2009) stated that, The Peter Principle would
realistically act in any organization where the mechanism of promotion, rewards the
best members and, where the mechanism at their new level in the hierarchical
structure, does not depend on the competence they had at the previous level, usually
because the tasks of the levels are very different to each other. They show, by means
of agent based simulations, that if the latter two features actually hold in a given
model of an organization with a hierarchical structure, then not only is the Peter
principle unavoidable, but also it yields in turn a significant reduction of the global
efficiency of the organization. Within a game theory-like approach, they explored
different promotion strategies and found, that in order to avoid such an effect the best
ways for improving the efficiency of a given organization are, either to promote each
time an agent at random or to promote randomly the best and the worst members in
terms of competence.
Thus it appears, at least according to the literature review performed in this study that
the Peter Principle is still thriving. It is evident that not much has truly changed in the
many years since Dr. Peter’s study in 1969.
2.5 Summary
This chapter introduces the Peter Principle and goes on to explain its various
characteristics which helps us to identify whether the effect exists in an organization.
It then discusses the possibility of the existence of this effect in software
development organizations. A software company is made up of employees who
either follow the management track or the technical track. The Peter Principle can be
present in two different scenarios. The first scenario is when a technical person gets
promoted to a managerial position and the second scenario is when incompetency
occurs in the technical positions itself. Both of the above should be avoided in a
23
software company for it to succeed. The chapter concludes with a discussion on
studies carried out by previous researchers on the Peter Principle effect.
24
CHAPTER 3 - METHODOLOGY
3.1 Introduction
This chapter discusses the methodology adopted for the study in detail to address the
research problem identified in Chapter 1. The research study carried out was
empirical in nature. An empirical study needs to be supported by theories so that
hypotheses can be generated and a basis can be given for interpreting and
summarizing the research results. Based on the review of the literature in Chapter 2,
this chapter describes the development of the conceptual model and the hypotheses
that guide the rest of the study and presents the methodology used in the study,
specifically in relation to the research design and the data collection process.
3.2 Conceptual Framework of the Study
After formulating the theoretical framework, the researcher has to develop the
conceptual framework of the study. A conceptual framework is described as a set of
broad ideas and principles taken from relevant fields of enquiry and used to structure
a subsequent presentation (Reichel & Ramey, 1987). It has potential usefulness as a
tool to scaffold research and, therefore, to assist a researcher to make meaning of
subsequent findings. Such a framework should be intended as a starting point for
reflection about the research and its context. A theoretical framework or the theory
on which the study is based was identified in Chapter 2. The conceptual framework
introduced in this section will be the operationalization of the identified theory. The
following Figure 3.1 indicates the key concepts associated with the study and their
relationships with each other.
25
Figure 3.1: Conceptual Model
According to the information gathered during the literature review, the above
relationships were derived from various researches carried out in the past.
The theory of Peter Principle and the criteria to identify its existence was introduced
by Peter and Hull (1969). The existence of a relationship between performance and
rewards management practices and Peter Principle was empirically studied by Gately
(1996); Faria (2000); Fairburn and Malcolmson (2000); Lazear (2004); King (2004);
Fetzer (2006); Chapman (2007) and Pluchino,Rapisarda and Garofalo(2009). The
existence of a relationship between selection and recruitment practices, was
described in the works of Koontz and Weihrich (1990). The relationship between
human resource development and Peter Principle was empirically studied by Minter
(1972) and Odiorne (1991). The relationship between the company structure and
Peter Principle was empirically studied by Peter and Hull (1969).
Quality of Performance and Rewards Management Practices
Quality of Selection and Recruitment Practices
Quality of Human Resource Development Practices
Company Structure
Company Size
Existence of
Peter Prinicple related
Behavior Patterns
26
3.3 Research Hypotheses
The hypothesis would indicate the predetermined relationship between the key
variables of the study and this would be based on the literature review ideas and the
understanding of the researcher on the considered subject area (Malhotra, 2007).
Thus, formulating the hypothesis would ensure that the study remains focused, and
on track as the researcher would have to seek to prove whether the hypothesis is
correct or incorrect. Formulating the hypothesis indicates that the researcher has a
preconceived idea and he would gather data and analyze them with the view of either
proving or disproving the hypothesis established. The following Table 3.1 indicates
the hypotheses which the study is seeking to prove.
Table 3.1: Hypothesis statements
Hypothesis
Description
H1 There is a significant relationship between the size of the company and the existence of the Peter Principle effect, where larger firms suffer more from this effect.
H2 There is a significant relationship between the structure of the company and the existence of the Peter Principle effect, where firms with tall structures suffer more from this effect.
H3 The quality of performance and rewards management practices in a company significantly, negatively influence the existence of Peter Principle effect
H4 The quality of recruitment practices in a company significantly, negatively influence the existence of Peter Principle effect
H5 The quality of human resource development practices in a company significantly, negatively influence the existence of Peter Principle effect
3.4 Research Model with Hypotheses
Following is the research model (Figure 3.2) with the introduction of the above
mentioned hypotheses. The graphical representation of the proposed framework
depicts the pattern and structure of relationships among the set of measured
variables. The purpose of the study was to measure relationships among these
variables. This research intended to investigate the existence of the Peter Principle
27
effect in Software Development firms of Sri Lanka, and the relationships between the
existence of the Peter Principle effect and company size, company structure, quality
of performance and rewards management practices, selection and recruitment
practices and human resource development practices.
Figure 3.2: Conceptual Model with Hypotheses
In the investigation, the existence of the Peter Principle effect was taken as the
dependent variable and, company size, company structure, quality of performance
and rewards management practices, quality of selection and recruitment practices and
quality of human resource development practices were taken as independent
variables. This research used a regression study to establish the existence of
relationships between the measured variables. As mentioned earlier the researcher’s
intention was to identify whether any relationships exists between these measured
H5
H4
H3
H2
H1
Existence of
Peter Prinicple related
Behavior Patterns
Quality of Performance and Rewards Management Practices
Quality of Selection and Recruitment Practices
Quality of Human Resource Development Practices
Company Structure
Company Size
28
variables. Regression study provides a measure of degree between two or more
variables. Therefore, the study was designed as a regression study.
3.5 Operationalization of Variables
This section intends to discuss the operationalization of the constructs identified in
the research model proposed for this study under section 3.4. These constructs must
be operational so as to enable the researcher to measure them. To do so, the abstract
notions of the constructs must be reduced into observable behavior or characteristics
(Sekaran, 2008). Operational definitions provide meaning to the constructs and a
tangible way to measure them.
In addition, constructs in the study uses multi items measures and a five point Likert
type scale. The constructs were adapted from the literature review carried out. The
following sections describe the definitions and item measures of the constructs.
3.5.1 The size of the company
As mentioned in Chapter 1, software development firms of Sri Lanka can be
categorized into three groups by size; small, medium and large. In general the size of
a business has been defined based on the number of employees. This differs from
country to country. More than 500, is generally considered to be a large business.
According to Campbell, (2007) some software organizations, such as Microsoft,
consider a small business as being up to 50 employees. Others consider anything
under 100 employees as a small business, and some consider anything under 500 a
small business. With respect to Sri Lankan software development companies, the
author defined less than 100 employees as small, between 100-300 employees as
medium and more than 300 employees as large size. The intention of the author was
to identify if there is a significant relationship between the size of a company and the
existence of the Peter Principle effect.
3.5.2 The structure of the company
29
Software development firms in Sri Lanka can be categorized as having tall or flat
hierarchies in a broad sense. According to Peter and Hull (1969), the Peter Principle
effect is more prevalent in tall hierarchies. The intention of the author was to identify
if there is a significant relationship between the structure of a company and the
existence of the Peter Principle effect.
3.5.3 Quality of performance and rewards management practices
Performance and rewards management practices are methods by which the job
performance of an employee is evaluated. It is the process of obtaining, analyzing,
and recording information about the relative worth of an employee to the
organization. It will analyze an employee's recent successes and failures, personal
strengths and weaknesses, and suitability for promotion or further training. The
intention of the author was to identify if there is a significant relationship between
the quality of performance and rewards management practices of a company and the
existence of the Peter Principle effect.
3.5.4 Quality of Selection and recruitment practices
Selection and recruitment of employees is an important aspect for any company. It
cannot be faulted as the success of any firm depends on the quality of human
resources or talents in that firm. Therefore it is very important for any company to be
very sure of hiring the right staff without compromising anything from the onset. The
intention of the author was to identify if there is a significant relationship between
the quality of selection and recruitment practices of a company and the existence of
the Peter Principle effect.
3.5.5 Human resource development practices
Human Resource Development is the integrated use of training, organization, and
career development efforts to improve individual, group and organizational
effectiveness. It develops the key competencies that enable individuals in
organizations to perform current and future jobs through planned learning activities.
The intention of the author was to identify if there is a significant relationship
30
between the human resource development practices of a company and the existence
of the Peter Principle effect.
The following table 3.2 depicts the operationalization of the identified variables.
31
Table 3.2 : Operationalization of the identified variables
Objectives Variables Indicators Sub Indicators Source-from Level of Measurement
Question Number
To identify if the Peter Principle effect exists in Software development firms of Sri Lanka
Peter Principle related behavior patterns
Percussive Sublimation
Incompetent employees promoted to reduce the harm done by them
(Peter& Hull,1996) Likert 9
Lateral Arabesque Isolating incompetent employees to reduce the harm done by them
(Peter& Hull,1996) Likert 9
Hierarchical Exfoliation
Competent employees are fired to preserve the hierarchy
(Peter& Hull,1996) Likert 12
Peter’s Inversion Existing process consistency is more valued than efficient service
(Peter& Hull,1996) Likert 11
Paternal In Step Employees are placed high up in the hierarchy based on personal relationships
(Peter& Hull,1996) Likert 13
Pull & Promotion Followers of Incompetent superiors get promoted easily
(Peter& Hull,1996) Likert 14
32
Frustration of incompetent employees
Frustration due to incompetence
(Peter& Hull,1996) Likert 19
Buck-Passing Shifting of responsibility or blame to anothers
(Peter& Hull,1996) Likert 16
Substitution of work
Substitute work to competent employees
(Peter& Hull,1996) Likert 17
Final Placement Syndrome
Employees who have achieved their highest level of incompetence
(Peter& Hull,1996) Likert 7,8,18
Work is accomplished by those employees who have not yet reached their level of incompetence
The number of competent employees in the hierarchy and their level of productivity
(Peter& Hull,1996) Likert 21,23
Rate of Incompetence at higher levels
The presence of incompetent employees
The number of incompetent employees in the hierarchy and their level of productivity
(Peter& Hull,1996) Likert 20,22
Dissatisfaction of competent employees
Frustration of competent employees
The turnover rate of competent employees
Author Developed Likert 10
Push & Promotion Competent employees have to push hard for promotions
(Peter& Hull,1996) Likert 15,24,25,26
33
To identify and analyze the impact of the determining factors that associate with the existence of Peter Principle effect
Size of the Organization
Size of the organization has an impact or not on the existence of Peter Principle
- Author Developed Likert 5
Company Structure Structure of the organization has an impact or not on the existence of Peter Principle
- Author Developed Likert 6
Performance & Rewards Management Practices
Having proper performance evaluation methods in place
- (Drucker,1993) Likert 27
Satisfaction of the employees on the performance evaluation method used
- Author Developed Likert 28
Clearly defined job responsibilities
- Author Developed Likert 29,30,31
Rewards based on performance ratings
- Author Developed Likert 32
Selection & Recruitment Practices
Proper job descriptions for job vacancies
- (Yate,1997) Likert 33
Well Structured Interviews that tests all aspects to select the best candidate
- (Yate,1997) Likert 34,36
34
Competent Interviewers
- Author Developed Likert 35
HR Development & Talent Management Practices
Good Training and Development Programs
- (Berger & Berger ,2003)
Likert 37,38,39
Employee Satisfaction towards training programs
- (Berger & Berger ,2003)
Likert 41
Competent Trainers
- Author Developed Likert 40
Demographics Age - - Author Developed Nominal 42Gender - - Author Developed Nominal 43Marital Status - - Author Developed Categorical 44Educational Qualifications
- - Author Developed Categorical 45
Designation - - Author Developed Categorical 2Experience in IT Industry
- - Author Developed Nominal 1
Experience in current company
- - Author Developed Nominal 3
Experience in current position
- - Author Developed Nominal 4
35
3.6 Unit of Analysis
As the study intended to investigate the effect of Peter Principle in the Software
Development firms of Sri Lanka, the study would cover a stratified selected sample
from the large, medium and small software development firms in the country.
3.6.1 Target Population
According to Sri Lanka Business Portal - Trade Information (2011), over 50,000 are
employed in the IT and BPO industry in Colombo. Therefore a stratified sampling
technique was used to select the sample. Sampling is the process of selecting a
sufficient number of elements from a population to represent the properties or
characteristics of that population (Sekaran, 2008, 226-227). A sample consisting of
381 employees was calculated as the ideal sample size for the above population. In
determining the sample size, Sekaran (2008, p.294) provided a table that generalized
scientific guideline for sample size decisions. According to the table, for a population
size of 50,000, the appropriate sample size is 381. This was calculated using a
confidence level of 95% and confidence interval of 5.
The sample was selected from the companies listed below in Table 3.3.
Table 3.3: Companies selected for the sample
Company Size (Large : Over 300 Medium:100-300Small : Less than 100)
Year of Establishment
Virtusa Large 1995IFS Large 1997MIT Large 1996Reservations Gateway
Medium 2001
Mubasher Medium 2000Aepona Medium 1999Creative Solutions Medium 1999Eurocenter Medium 2000Ecollege Medium 2004Aeturnum Medium 2001WSO2 Small 2005B Sharp Lanka Small 2003Interblocks Small 2000
36
Sabre Technologies Small 2001EDM Systems Small 1995
The selected sample mostly included employees who are in the lower levels of the
hierarchy and in the middle level management positions such as technical leads,
quality assurance leads, project managers, architects etc. Such employees were
selected as there is a high possibility that they have not reached their level of
incompetence, thus enabling the researcher to obtain more accurate responses.
3.6.2 Data collection instrument
A research survey will be only as good as the questions it asks. Questionnaire design,
therefore, is one of the most critical stages in the research process. According to
Sekaran (2008), a good questionnaire design should focus upon three areas; the
wording of the questions, the principle of measurements, the general appearance of
the questionnaire. Taking the above into consideration a structured questionnaire was
designed in order to collect the required data for the investigation.
The questionnaire was designed only in English language as it was fair to assume
that all employees in software development firms will have the general
understanding of English language.
The questionnaire was made up of two key areas: the core area and the demographics
area. Under demographics, all demographic related information was collected. This
information included the age, gender, duration of work and other personal
information related to the respondent. Even though this information would not have a
direct relevance to the research study, it was used to understand the demographic
profile of the respondents and was used as cross analysis points where further
analysis of the research data was required.
The core area consisted of the questions, which had direct relevance to the key
information areas the study attempted to cover. That is to identify the existence of
Peter Principle effect, and the contribution of the factors listed in section 3.5, to its
existence. All core area questions would take the form of Likert scale based
questions where the respondents could indicate the levels of agreement using a five
37
point Likert scale. According to Sekaran(2008), the advantage of using the Likert
scale is that the respondents would have the freedom to express their views using a
range of alternatives and the response would be focused and easy to directly use for
analysis purposes.
3.6.3 The method of data collection and analysis
The questionnaire was distributed as a web based questionnaire. The researcher was
able to collect a total of 396 valid responses from the survey carried out. The
questionnaire used can be found under Appendix-A.
The responses from the questionnaire were used to study whether the Peter Principle
effect exists in Sri Lankan software development firms and to identify the
contribution of the factors identified in the literature review to its existence.
Proposing recommendations to avoid the Peter Principle, which was the final
objective of this study, was based on the entire output of first and second objectives.
The raw data set that was obtained from the sampled questionnaires was fed in to the
Statistical Package for Social Sciences (SPSS version 15) software and various
statistical analysis methods like, correlation analysis and regression analysis were
carried out.
3.7. Summary
This chapter discusses the methodology adopted for the study. It gives a
comprehensive explanation on the conceptual model that was created and the
hypotheses that the researcher intends to test. It then discusses the operationalization
of the variables identified and the target population selected. It concludes with an
explanation on the data collection and analysis method selected to carry out the
study.
38
CHAPTER 4 – DATA ANALYSIS AND DISCUSSION
4.1 Introduction
This chapter presents the data and a discussion of the findings. The findings
presented under this section are derived from the data gathered from the research
questionnaire. The study was exclusively limited to employees of the software
development firms mentioned in chapter 3. First section of the chapter will highlight
the general information and demographics of the studied sample. In the second
section of the study reliability and the validity analysis of the identified constructs
are discussed. In the next sections of this chapter, descriptive statistics, correlation
analysis will be presented followed by the regression analysis among the constructs
identified.
4.2 Characteristics of the Sample
This section analyzes the general characteristics of the sample in consideration. It is
presented in two sections as characteristics of the organizations and the respondents.
4.2.1 Characteristics of the organizations
The characteristics of the organizations in the sample are given below. In question 5
(Refer Appendix A) the respondents were requested to state the size of their
respective organizations. The summary of the responses to the said question is as
follows. From the total of 396 respondents, 32.8% were from companies had less
than 100 employees, 31.6% were from companies that had employees between 100
and 300 and the remaining 35.6% were from companies that had over 300
employees. The Table 4.1 summarizes the data obtained.
39
Table 4.1: Summary table for number of employees in organizations
Frequency Percent Valid PercentCumulative
PercentLess than 100 130 32.8 32.8 32.8
Between 100 and 300 125 31.6 31.6 64.4
Greater than 300 141 35.6 35.6 100.0
Total 396 100.0 100.0
In question 6 (Refer Appendix A) the respondents were asked about the structure of
their company. The summary of the responses to the said question is as follows.
From the total of 396 respondents, 50.3% mentioned that their company’s hierarchy
is flat and the remaining 49.7% said it was tall. The table 4.2 summarizes the data
obtained.
Table 4.2: Summary table for number of employees in different organization structures
Frequency Percent Valid PercentCumulative
PercentFlat 199 50.3 50.3 50.3
Tall 197 49.7 49.7 100.0
Total 396 100.0 100.0
4.2.2 Characteristics of the respondents
This section presents the key demographic features of the respondents who
participated in the survey.
Age
From the responses given to question 42 (Refer Appendix A), the age distribution of
the respondents is as follows. From the 396 respondents 10.6% were in the age group
below 25, 87.1% were in the age group between 26 and 35. Therefore the majority of
the respondents were in their 20s and 30s. 2% were in the group between 36 and 45
40
and the remaining 0.3% were in the above 45 group. The table 4.3 summarizes the
data obtained.
Table 4.3: Summary table for the age distribution of the respondents
Frequency Percent Valid PercentCumulative
PercentBelow 25 42 10.6 10.6 10.6
Between 25 and 35 345 87.1 31.6 97.7
Between 36 and 45 8 2.0 2.0 99.7
Above 45 1 0.3 0.3 100.0Total 396 100.0 100.0
Gender
Question 43 (Refer Appendix A), inquired about the gender of the respondents. From
the 396 respondents 55.8% were male and 44.2% were female. The table 4.4
summarizes the data obtained.
Table 4.4: Summary table for the gender distribution of the respondents
Frequency Percent Valid PercentCumulative
PercentMale 221 55.8 55.8 55.8
Female 175 44.2 44.2 100.0
Total 396 100.0 100.0
Marital Status
From the responses to the question 43 (Refer Appendix A),, the marital statuses of
the respondents are as follows. From the 396 respondents, 77.8% were single and
22.2% were married. The table 4.5 summarizes the data obtained.
Table 4.5: Summary table for the marital status of the respondents
Frequency Percent Valid PercentCumulative
PercentSingle 308 77.8 77.8 77.8
Married 88 22.2 22.2 100.0
Total 396 100.0 100.0
41
Education Level
Question 44(Refer Appendix A), inquired about the education level of the
respondents. From the 396 respondents, 0.5% had advanced level or below
qualifications. These could probably be trainees who have not yet completed their
degrees. 87.6% had bachelor’s degrees or equivalent. The remaining 11.9% had
postgraduate qualifications. The table 4.6 summarizes the data obtained.
Table 4.6: Summary table for the education level of the respondents
Frequency Percent Valid PercentCumulative
PercentAdvanced Level or below 2 .5 .5 .5
Bachelor's Degree or equivalent 347 87.6 87.6 88.1
Postgraduate Qualifications47 11.9 11.9 100.0
Total 396 100.0 100.0
Current Designation
Question 2 inquired about the current designation of the employees. From the
responses given, 12.4% were in the Trainee Level (e.g. associate software engineers,
associate QA engineers etc), 42.9% were in the intermediate level (e.g. software
engineers, QA engineers, business analysts etc) 31.6% were in the senior level (e.g.
senior software engineers, senior business analysts etc), 8.1% were in the lead level
(e.g. tech leads, QA consultants etc) and the remaining 5.1% (e.g. project managers,
architects etc) were in the managerial level. The designations were split into the
above four broad categories as the main intention of the author was to find the level
of the employee’s position in the hierarchy. The Table 4.7 summarizes the data
obtained.
42
Table 4.7: Summary table for the designation level of the respondents
Frequency Percent Valid PercentCumulative
PercentTrainees/Associate Level 49 12.4 12.4 12.4
Intermediate Level 170 42.9 42.9 55.3
Senior Level 125 31.6 31.6 86.9
Lead Level 32 8.1 8.1 94.9
Managerial Level 20 5.1 5.1 100.0
Total 396 100.0 100.0
Years of experience in IT
The total years of experience of the respondent was inquired by question 1. The
responses are as follows. 11.9% had below 2 years of experience in total, 65.9%
were in the 2-5 years range, 19.7% were in the 5-10 years range and the remaining
2.5% were in the over 10 years range. The table 4.8 summarizes the data obtained.
Table 4.8: Summary table for the years of experience in IT of the respondents
Frequency Percent Valid PercentCumulative
PercentBelow 2 years 47 11.9 11.9 11.9
Between 2-5 years 261 65.9 65.9 77.8
Between 5 -10 years 78 19.7 19.7 97.5
Above 10 years 10 2.5 2.5 100.0Total 396 100.0 100.0
Years of experience in the current company
Question 3 inquired about the years of experience in the current company. The
responses are as follows. 19.7% had below 2 years of experience, 69.9% were in the
2-5 years range, 9.1% were in the 5-10 years range and the remaining 1.3% were in
the over 10 years range. The table 4.9 summarizes the data obtained.
43
Table 4.9: Summary table for the years of experience in the current company of the respondents
Frequency Percent Valid PercentCumulative
PercentBelow 2 years 78 19.7 19.7 19.7
Between 2-5 years 277 69.9 69.9 89.6
Between 5 -10 years 36 9.1 9.1 98.7
Above 10 years 5 1.3 1.3 100.0
Total 396 100.0 100.0
Years of experience in the current post
Question 4 inquired about the years of experience in the current post. The responses
are as follows. 61.6% had below 2 years of experience, 36.8% were in the 2-5 years
range, 1.3% were in the 5-10 years range and the remaining 0.3% were in the over 10
years range. The table 4.10 summarizes the data obtained.
Table 4.10: Summary table for the years of experience in the current post of the respondents
Frequency Percent Valid PercentCumulative
PercentBelow 2 years 244 61.6 61.6 61.6
Between 2-5 years 146 36.8 36.8 98.4
Between 5 -10 years 5 1.3 1.3 99.7
Above 10 years 1 0.3 0.3 100.0
Total 396 100.0 100.0
4.3 Goodness-of-Fit Measures
Reliability analysis was carried out for all variables in the conceptual framework to
test reliability and the consistency of data. The Cronbach’s alpha indicates how well
the items in a set are positively correlated to one another. The closer the reliability
reaches 1.0, the better the reliability and validity. Generally, reliabilities less than 0.6
are considered poor. Those in the range of 0.7 are acceptable and those over 0.8 good
(Sekaran 2008, p311).
44
Principle component analysis was used to identify the underlying components that
explain the pattern of correlations within a set of observed variables. It is used in data
reduction to identify a small number of factors that explain most of the variance
observed in a much larger number of variables.
The results of the tests carried out and the decisions taken will be discussed in the
following sections.
4.3.1 Reliability and validity analysis - the existence of Peter Principle effect
The results of the reliability analysis carried out on the factors that indicate the
existence of Peter Principle effect are as follows. Refer Appendix A for the
descriptions of the questions.
Table 4.11: Reliability analysis for the factors that determine the existence of Peter Principle effect
Concept/Variable Questions Retained Questions Rejected
Cronbach’s Alpha Value
Peter Principle Behaviors
Q7,Q8,Q9, Q10,Q11,Q12,Q13,Q14, Q15,Q16,Q17,Q18,Q19,Q20,Q22
Q21,Q23,Q24,Q25,Q26
0.96
Q21,Q23,Q24,Q25 and Q26 were very weakly correlated with the rest of the
variables. Hence they were removed .The Cronbach alpha value increased to 0.96
after the removal. (Refer appendix B)
Principle component analysis was carried out to identify the underlying components
(Refer appendix B). The results obtained from the analysis is shown in table 4.12:
45
Table 4.12: Principle component analysis for the factors that determine the existence of Peter Principle effect
Component Initial EigenvaluesExtraction Sums of Squared
LoadingsRotation Sums of Squared
Loadings
Total% of
VarianceCumulative
% Total% of
VarianceCumulative
% Total% of
VarianceCumulative
%1 9.432 62.883 62.883 9.432 62.883 62.883 6.550 43.668 43.6682 1.321 8.805 71.687 1.321 8.805 71.687 4.203 28.019 71.687
3 0.759 5.063 76.750
4 0.589 3.929 80.679
5 0.485 3.235 83.914
6 0.480 3.198 87.113
7 0.390 2.601 89.714
8 0.315 2.100 91.815
9 0.274 1.825 93.640
10 0.247 1.647 95.288
11 0.210 1.399 96.686
12 0.183 1.221 97.907
13 0.136 0.909 98.816
14 0.122 0.816 99.632
15 0.055 0.368 100.000
The analysis showed that there were two components with high factor loadings.
Those two components together explained 71.7% of the variance. The factor loadings
on each of the components are as follows.
Table 4.13: Rotated component matrix for the factors that determine the existence of Peter Principle effect
Component
1 2Q7 0.704 0.471Q8 0.885Q9 0.689 0.436Q10 0.581 0.445Q11 0.781 Q12 0.745 Q13 0.855 Q14 0.630Q15 0.546 0.430Q16 0.831 Q17 0.835 Q18 0.839 Q19 0.731 Q20 0.900Q22 0.880
Thus the variables were split into two groups based on the factor loadings above.
46
Existence of Peter Principle related behavior patterns
Q7,Q9,Q10,Q11,Q12,Q13,Q15,Q16,Q17,Q18 and Q19 loaded heavily on component one. They mainly explained the existence of Peter Principle related behavior patterns.
Existence of incompetent employees in the higher levels of the company hierarchyQ8, Q14, Q20, Q22 loaded highly on component two. These variables explained the existence of incompetent employees in the higher levels of software development firms.
Considering the component breakdown above there was a need to adjust the
initial conceptual framework accordingly. The revised conceptual framework is
shown in Figure 4.1:
Figure 4.1 Revised conceptual model
Existence of Peter Principle related behavior patterns
Existence of incompetent Employees in the higher levels of the company hierarchy
Quality of Performance and Rewards Management Practices
Quality of Selection and Recruitment Practices
Quality of Human Resource Development Practices
Company Structure
Company Size
H5
H4
H3
H2
H1
47
4.3.2 Reliability and validity analysis - the contributing factors for the existence of Peter Principle effect
The results of the reliability analysis carried out on the contributing factors for the
existence of Peter Principle effect are as follows.
a) Quality of performance and rewards management practices
Table 4.14: Reliability analysis results for quality of performance and rewards management practices related variables
Concept/Variable Questions Retained Questions Rejected Cronbach’s Alpha Value
Evaluation and Rewards Management Practices
Q27, Q28, Q29 , Q30, Q31, Q32
- 0.91
All the variables in concern were highly correlated to each other. Therefore none of
them were rejected. The Cronbach alpha value obtained for the set of variables that
explained the quality of performance and rewards management practices in
companies was 0.91, which is a very high value and it shows that the variables
considered are highly reliable. (Refer appendix B for more details on the analysis)
The principle component analysis gave the following results.
Table 4.15: Principle component analysis results for quality of performance and rewards management practices related variables
Component
Initial Eigen values Extraction Sums of Squared Loadings
Total % of Variance Cumulative % Total % of Variance Cumulative %1 4.209 70.144 70.144 4.209 70.144 70.1442 0.680 11.331 81.4753 0.365 6.090 87.5654 0.301 5.024 92.5895 0.273 4.557 97.1476 0.171 2.853 100.000
48
The analysis showed that there was one component with high factor loadings. It
explained 70.1% of the variance. The factor loadings are as follows:
Table 4.16: Factor loadings for quality of performance and rewards management practices related variables
Component
1Q27 0.832Q28 0.829Q29 0.847Q30 0.871Q31 0.819Q32 0.825
All the factors loaded well on the component. Hence none of the factors were rejected.
b) Quality of recruitment management practices
Table 4.17: Reliability analysis results for quality of recruitment management practices related variables
Concept/Variable Questions Retained Questions Rejected Cronbach’s Alpha Value
Evaluation and Rewards Management Practices
Q33, Q34, Q35 , Q36 - 0.88
All the variables in concern were highly correlated to each other. Therefore none of
them were rejected. The Cronbach alpha value obtained for the set of variables that
explained the quality of recruitment practices in companies was 0.88, which is a very
high value and it shows that the variables considered are highly reliable.
49
The principle component analysis gave the following results.
Table 4.18: Principle component analysis results for quality of recruitment management practices related variables
Component
Initial Eigen values Extraction Sums of Squared Loadings
Total % of Variance Cumulative % Total % of Variance Cumulative %1 2.943 73.574 73.574 2.943 73.574 73.5742 .458 11.439 85.0143 .363 9.085 94.0984 .236 5.902 100.000
The analysis showed that there was one component with high factor loadings. It
explained 73.6% of the variance. The factor loadings are as follows.
Table 4.19: Factor loadings for quality of recruitment management practices related variables
Component
1Q33 0.820Q34 0.852Q35 0.889Q36 0.868
All the factors loaded well on the component. Hence none of the factors were rejected.
c) Quality of human resource development practicesTable 4.20: Reliability analysis results for quality of human resource development practices related variables
Concept/Variable Questions Retained Questions Rejected
Cronbach’s Alpha Value
Evaluation and Rewards Management Practices
Q37, Q38, Q39 , Q40, Q41 - 0.92
All the variables in concern were highly correlated to each other. Therefore none of
them were rejected. The Cronbach alpha value obtained for the set of variables that
explained the quality of human resource development practices in companies was
50
0.92, which is a very high value and it shows that the variables considered are highly
reliable.
Principle component analysis results for the above sets of variables are as follows.
Table 4.21: Principle component analysis results for quality of human resource development practices related variables
Component
Initial Eigen values Extraction Sums of Squared Loadings
Total % of Variance Cumulative % Total % of Variance Cumulative %1 3.839 76.789 76.789 3.839 76.789 76.7892 .468 9.363 86.1523 .313 6.256 92.4074 .241 4.829 97.2375 .138 2.763 100.000
The analysis showed that there was one component with high factor loadings. It
explained 76.8% of the variance. The factor loadings are as follows.
Table 4.22: Factor loadings for quality of human resource development practices related variables
Component 1Q37 0.887Q38 0.852Q39 0.837Q40 0.885Q41 0.918
All the factors loaded well on the component. Hence none of the factors were rejected.
4.4 Descriptive Statistics
Measure of central tendency and dispersions provide a way to have a feel for the
collected data set. Descriptive statistics such as maximum, minimum, means,
standard deviations and variance were obtained for the independent variables that
described the Peter Principle effect and the factors that contributed to its existence.
51
The values mean and standard deviation values obtained are shown in the below
tables. (Refer Appendix B for additional information). It should be noted that all
variables were measured on five point Likert scale ranging from 1-Very Less to 5-
Very High and the total numbers of subjects in the selected sample was 396.
The descriptive statistics obtained for the factors that explain the existence of the
Peter Principle effect are as follows.
Table 4.23: Descriptive statistics for the factors that explain the existence of Peter Principle effect
Question Mean Std. DeviationExistence of previously competent employees who have become incompetent after obtaining promotions (Final placement syndrome) 3.17 1.406
Existence of incompetent employees who could not obtain further promotions (Final placement syndrome) 3.13 0.983
Existence of employees who are promoted to minimize the harm done by them in the current position(Percussive sublimation) 2.76 1.221
The rate of competent employees leaving the organization3.55 1.269
Discouraging innovative ideas and processes to preserve the organization’s existing practices(Peter’s inversion) 3.15 1.396
Highly competent employees being fired to preserve the company’s hierarchy (Hierarchical Exfoliation) 2.61 1.269
Promotions given based on personal relationships(Paternal In Step)3.16 1.496
Followers of superiors getting promoted regardless of their competency level (Pull and Promotion) 3.16 1.350
Competent employees have to push hard to get promoted (Push and Promotion)3.09 1.038
Existence of employees who shift the blame on to others for their actions(Buck Passing) 3.30 1.244
Existence of incompetent superiors who substitute work to the subordinates under them 3.38 1.284
Existence of employees who ignore their duties and focus more on activities that are not direct responsibilities of their position(Final placement syndrome) 3.17 1.311
Existence of employees who are overly stressed, mentally disturbed and frequently sick even though they do not do a lot of productive work. (Final placement syndrome)
2.96 1.052
The number of incompetent employees in the higher levels of organizations3.07 1.000
The productivity of the employees in the higher levels of the organization hierarchies 3.09 0.957
52
The mean values obtained above are in the range of 2.61 – 3.55. This implies that the
Peter Principle moderately exists in the software development firms of Sri Lanka.
Hierarchal exfoliation and percussive sublimation has a lesser value compared to the
other factors but it doesn’t imply that those behaviors are nonexistent. The lesser
value maybe due to the fact that, it is not ethical to carry out the above activities or
maybe because the employees who come under those categories resign prior to the
occurrence of the above.
The descriptive statistics obtained for the factors that explain the perspective of the
employees of the quality of the performance and rewards management practices in
their respective organizations are as follows.
Table 4.24: Descriptive statistics for the factors that explain the quality of performance and rewards management practices
Question Mean Std. DeviationThe organization has a standard way of evaluating employee performance
3.02 1.058
Employee satisfaction with the current performance evaluation methods in the organization 2.75 0.950
The organization maintains proper job descriptions for all positions2.88 1.051
The organization clearly communicates job responsibilities to the employees3.06 1.062
The work performed by the employees match their job descriptions3.03 1.027
The organization provides rewards/promotions solely based on performance ratings2.97 1.030
The mean values obtained above are in the range of 2.75 – 3.06. This implies that the
quality of performance evaluation and rewards management practices in
organizations are in a moderate level and improvement is needed for the
organizations to reach their maximum potential.
The descriptive statistics obtained for the factors that explain the perspective of the
employees of the quality of recruitment and selection practices in their respective
organizations are as follows.
53
Table 4.25: Descriptive statistics for the factors that explain the quality of selection and recruitment practices
Mea
nStd.
DeviationThe organization provides comprehensive job descriptions when advertising jobs
3.07 0.966
Selection interviews are well structured to identify the best candidates3.02 1.035
Members of the selection panel are competent and properly trained3.01 1.010
The organization gives due attention to skills like teamwork, leadership, attitude etc when selecting a new employee 3.10 1.018
The mean values obtained above are in the range of 3.01 – 3.10. This implies that the
quality of selection and recruitment practices in organizations are in a moderate level
and improvement is needed for the organizations to reach their maximum potential.
The descriptive statistics obtained for the factors that explain the perspective of the
employees of the quality of human resource development practices in their respective
organizations are as follows.
Table 4.26: Descriptive statistics for the factors that explain the quality of human resource development practices
Question Mean Std. DeviationThe organization provides job related trainings for all employees on technical matters 3.01 1.119
The organization provides training on soft skills2.86 1.090
The organization has a well defined HR development /talent management process to achieve organizational goals 2.72 1.054
The trainers in the organization are highly competent2.96 1.058
The employees are satisfied with the training they receive2.89 1.016
The mean values obtained above are in the range of 2.72 – 3.01. This implies that the
quality of human resource development practices in organizations is in a moderate
level and improvement is needed for the organizations to reach their maximum
potential.
54
4.5 Correlation Analysis
Correlation analysis was performed on the two dependent and five independent
variables identified during the conceptual model.
The two dependent variables are
The existence of Peter Principle related behavior patterns (AvPPB)
The existence of incompetent employees in the higher levels of the company hierarchy (AvIEHL)
The five independent variables are:
The size of the company(Q5)
The structure of the company(Q6)
The quality of performance and rewards management practices in a
company(AvPRM)
The quality of recruitment practices in a company(AvRP)
The quality of human resource development practices in a company(AvHRD)
The correlation analysis results for the above are shown in the below table
followed by the interpretation of the results.
55
Table 4.27: Correlation analysis results
AvPRM AvRP AvHRD AvPPB AvIEHL Q5 Q6AvPRM
1 0.811** 0.794** -0.721** -0.495** 0.059 -0.436**
AvRP
0.811** 1 0.739** -0.691** -0.508** 0.066 -0.453**
AvHRD
0.794** 0.739** 1 -0.587** -0.381** 0.147** -0.332**
AvPPB
-0.721** -0.691** -0.587** 1 0.719** -0.019 0.564**
AvIEHL
-0.495** -0.508** -0.381** 0.719** 1 -0.048 0.416**
Q5
0.059 0.066 0.147** -0.019 -0.048 1 0.077
Q6
-0.436** -0.453** -0.332** 0.564** 0.416** 0.077 1
According to the results shown in table 4.26, the following were identified:
The significant value of the correlation between the size of a company(Q5)
and the existence of Peter Principle related behavior patterns(AvPPB) is
0.711, which is greater than 0.05. Hence we can conclude that there is no
significant correlation between the two factors in consideration.
The significant value of the correlation between the size of a company(Q5)
and the existence of incompetent employees in the higher levels of the
company hierarchy(AvIEHL) is 0.344, which is greater than 0.05. Hence we
can conclude that there is no significant correlation between the two factors
in consideration.
56
The significant value of the correlation between the structure of a
company(Q6) and the existence of Peter Principle related behavior
patterns(AvPPB) is 0.001, which is less than 0.05. Further the Pearson
correlation coefficient for above-mentioned relationship is 0.564. Hence we
can conclude that there is a strong significant positive correlation between the
two factors in consideration.
The significant value of the correlation between the structure of a
company(Q6) and the existence of incompetent employees in the higher
levels of the company hierarchy(AvIEHL) is 0.001, which is less than 0.05.
Further the Pearson correlation coefficient for above-mentioned relationship
is 0.416. Hence we can conclude that there is a significant positive correlation
between the two factors in consideration.
The significant value of the correlation between the quality of performance
and rewards management practices (AvPRM) of a company and the existence
of Peter Principle related behavior patterns (AvPPB) is 0.001, which is less
than 0.05. Further the Pearson correlation coefficient for above-mentioned
relationship is -0.721. Hence we can conclude that there is a strong
significant negative correlation between the two factors in consideration.
The significant value of the correlation between the quality of performance
and rewards management practices of a company (AvPRM) and the existence
of incompetent employees in the higher levels of the company hierarchy
(AvIEHL) is 0.001, which is less than 0.05. Further the Pearson correlation
coefficient for above mentioned relationship is -0.495. Hence we can
conclude that there is a significant negative correlation between the two
factors in consideration.
The significant value of the correlation between the quality of recruitment
practices (AvRP) of a company and the existence of Peter Principle related
behavior patterns (AvPPB) is 0.001, which is less than 0.05. Further the
57
Pearson correlation coefficient for above-mentioned relationship is -0.691.
Hence we can conclude that there is a strong significant negative correlation
between the two factors in consideration.
The significant value of the correlation between the quality of recruitment
practices of a company (AvRP) and the existence of incompetent employees
in the higher levels of the company hierarchy (AvIEHL) is 0.001, which is
less than 0.05. Further the Pearson correlation coefficient for above-
mentioned relationship is -0.508. Hence we can conclude that there is a
strong significant negative correlation between the two factors in
consideration.
The significant value of the correlation between the human resource
development practices of a company (AvHRD) and the existence of Peter
Principle related behavior patterns (AvPPB) is 0.001, which is less than 0.05.
Further the Pearson correlation coefficient for above mentioned relationship
is -0.587. Hence we can conclude that there is a strong significant negative
correlation between the two factors in consideration.
The significant value of the correlation between the human resource
development practices of a company (AvHRD) and the existence of
incompetent employees in the higher levels of the company hierarchy
(AvIEHL) is 0.001, which is less than 0.05. Further the Pearson correlation
coefficient for above-mentioned relationship is -0.381. Hence we can
conclude that there is a significant negative relationship between the two
factors in consideration.
58
4.6 Multiple Regression Analysis
Correlation coefficient r indicates the strength of the relationship between two
variables, but it does not give an idea of how much of the variance in the dependent
variable will be explained, when several independent variables simultaneously
influence it. Multiple regression analysis is used to analyze such situations (Sekaran
2008, p405).
Multiple regression analysis was used in this study to analyze the relationships when
all independent variables simultaneously influence the dependent variable. It is
conducted to examine how well those independent variables predict the dependent
variable when taken as a model. As two components were identified as the
determining factors of Peter Principle during section 4.3.1, multiple regression
analysis was performed for each dependent variable.
Multiple regression analysis results for the dependent variable “existence of Peter
Principle related behavior patterns” and the independent variables “size of the
company” , “structure of the company”, “quality of performance and rewards
management practices in a company” , “quality of recruitment practices in a
company” and quality of “human resource development practices in a company” ,
are given below.
Table 4.28: Model summary for the existence of Peter Principle related behavior patterns and the contributing factors
Model R R SquareAdjusted R
SquareStd. Error of the
Estimate1 0.784(a) 0.614 0.609 0.65614
a Predictors: (Constant), Q6, Q5, AvHRD, AvRP, AvPRM
Q5 - The size of the companyQ6 - The structure of the companyAvHRD - The quality of performance and rewards management practices in a companyAvRP - The quality of recruitment practices in a companyAvPRM - The quality of human resource development practices in a company
59
Table 4.29: ANOVA table for existence of Peter Principle related behavior patterns and the contributing factors
Model Sum of Squares df Mean Square F Sig.
1 Regression 267.197 5 53.439 124.127 .000(a)
Residual 167.904 390 .431
Total 435.101 395
a Predictors: (Constant), Q6, Q5, AvHRD, AvRP, AvPRMb Dependent Variable: AvPPB
Q5 - The size of the companyQ6 - The structure of the companyAvHRD - The quality of performance and rewards management practices in a companyAvRP - The quality of recruitment practices in a companyAvPRM - The quality of human resource development practices in a companyAvPPB - The existence of Peter Principle related behavior patterns
Table 4.30: Coefficients for existence of Peter Principle related behavior patterns and the contributing factors
Model
Unstandardized Coefficients
Standardized Coefficients t Sig.
B Std. Error Beta B Std. Error1 (Constant) 5.163 .172 29.973 .000
AvPRM -.508 .076 -.417 -6.651 .000
AvRP -.284 .069 -.234 -4.101 .000
AvHRD .011 .061 .010 .181 .856
Q5 -.002 .041 -.001 -.041 .967
Q6 .586 .075 .280 7.781 .000
a Dependent Variable: AvPPB
AvPPB - The existence of Peter Principle related behavior patterns
The model summary table 4.27 above provides the R and R2 values for the
relationships between the said dependent and independent variables. R or the
correlation coefficient indicates the strength of the relationship. In this case the R-
value indicates multiple R, which is the correlation of all the independent variables
against dependent variable, which is 0.784. This is quite a high value and it confirms
there is a strong relationship between the variables. The value of the R2 gives the
amount of variance explained by these models. In this case the R2 value is 0.614
which means the independent variables together (the model) explain 61.4% of the
variance in the existence of Peter Principle related behavior patterns. Hence we can
60
conclude that the independent variables considered largely define why the Peter
Principle effect exists.
The Coefficients table 4.29 shows Beta values for all the independent variables when
they are regressed jointly against dependent variables. The independent variables,
“quality of performance and rewards management practices”, “quality of selection
and recruitment practices” have negative Beta values. Therefore it shows a negative
relationship towards the dependent variables. The results imply that an increase in
the above two independent variables will inherently decrease the dependent variable.
Even though the independent variable quality of human resource development
practices had a significant correlation when correlated individually with the
dependent variable, it proves to be insignificant when jointly regressed with the other
independent variables. The size of the company proves to be insignificant. Hence
there is no relationship between the size of a company and the existence of Peter
Principle effect related behaviors patterns. The structure of a company has a
significant relationship and the Peter Principle effect related behavior patterns seems
to be high in companies that have tall structures.
Multiple regression analysis results for the dependent variable “existence of
incompetent employees in the higher levels of the company hierarchy” and the
independent variables “size of the company”, “structure of the company”, “quality of
performance and rewards management practices in a company”, “quality of
recruitment practices in a company” and “quality of human resource development
practices in a company”, are given below.
Table 4.31: Model Summary for the existence of incompetent employees in the higher levels of the hierarchy and the contributing factors
R R SquareAdjusted R
SquareStd. Error of the Estimate
1 .566(a) .320 .311 .78509
a Predictors: (Constant), Q6, Q5, AvHRD, AvRP, AvPRM
Q5 - The size of the companyQ6 - The structure of the companyAvHRD - The quality of performance and rewards management practices in a companyAvRP - The quality of recruitment practices in a companyAvPRM - The quality of human resource development practices in a company
61
Table 4.32: ANOVA table for the existence of incompetent employees in the higher levels of the hierarchy and the contributing factors
Model Sum of Squares df Mean Square F Sig.
1 Regression 113.120 5 22.624 36.705 .000(a)
Residual 240.385 390 .616
Total 353.506 395
a Predictors: (Constant), Q6, Q5, AvHRD, AvRP, AvPRMb Dependent Variable: AvIEHL
Q5 - The size of the companyQ6 - The structure of the companyAvHRD - The quality of performance and rewards management practices in a companyAvRP - The quality of recruitment practices in a companyAvPRM - The quality of human resource development practices in a companyAvIEHL - The existence of incompetent employees in the higher levels of the company hierarchy
Table 4.33: Coefficients for the existence of incompetent employees in the higher
levels of the hierarchy and the contributing factors
Model
Unstandardized Coefficients
Standardized Coefficients t Sig.
B Std. Error Beta B Std. Error1 (Constant) 4.464 .206 21.657 .000
AvPRM -.283 .091 -.258 -3.098 .002
AvRP -.304 .083 -.278 -3.669 .000
AvHRD .109 .073 .108 1.488 .138
Q5 -.053 .049 -.046 -1.088 .277
Q6 .409 .090 .217 4.543 .000
a Dependent Variable: AvIEHL
AvIEHL - The existence of incompetent employees in the higher levels of the company hierarchy
In this case the R-value, is 0.566. This is quite a high value and it confirms there is a
strong relationship between the variables. The R2 value is 0.320 which means the
independent variables together explains only 32% of the variance as to why there are
incompetent employees in higher levels of the hierarchy. This could be due to the
reason that there may be more reasons why incompetent employees exist in the
higher levels of the hierarchy than the ones considered here.
The Coefficients tables show Beta values for all the independent variables when they
are regressed jointly against dependent variables. The independent variables, “quality
62
of performance and rewards management practices”, “quality of selection and
recruitment practices” have negative Beta values in this case too. Therefore it shows
a negative relationship towards the dependent variable. The results imply that an
increase in the above two independent variables, will inherently decrease the
dependent variable. Even though the independent variable “quality of human
resource development practices” had a significant correlation when correlated
individually with the dependent variable, it proves to be insignificant when jointly
regressed with the other independent variables. The size of the company proves to be
insignificant. Hence there is no relationship between the size of a company and the
existence of incompetent employees in the higher levels of the company hierarchy.
The structure of a company has a significant relationship and incompetent employees
in the higher levels of the company hierarchy seem to be high in companies that have
tall structures.
The software development companies considered for the study were divided into two
main groups considering the structure of the company as “Tall” or “Flat” in a broad
sense. As the data collected for this aspect is categorical in nature, the Independent
sample’s T-Test was used to test the relationship between the structure of a company
and the existence of the Peter Principle effect. The results obtained from the test are
as follows.
Table 4.34: Group Statistics for structure of the company
Q6 N Mean Std. DeviationStd. Error
MeanAvPPB Flat 199 2.5313 .72411 .05133
Tall 197 3.7134 .99216 .07069AvIEHL Flat 199 2.7224 .81448 .05774
Tall 197 3.5076 .90665 .06460
AvPPB - The existence of Peter Principle related behavior patterns
AvIEHL - The existence of incompetent employees in the higher levels of the company hierarchy
According to the results obtained, it can be seen that the mean values obtained for
existence of the Peter Principle related behavior patterns and incompetent employees
63
in higher levels of the hierarchy is comparatively less for companies with flat
hierarchies.
Table 4.35: Independent Sample Test for structure of the companyIndependent Samples Test
30.858 .000 -13.553 394 .000 -1.18214 .08722 -1.35362 -1.01065
-13.532 358.522 .000 -1.18214 .08736 -1.35394 -1.01033
6.416 .012 -9.068 394 .000 -.78525 .08659 -.95549 -.61501
-9.064 388.691 .000 -.78525 .08664 -.95559 -.61491
Equal variancesassumed
Equal variancesnot assumed
Equal variancesassumed
Equal variancesnot assumed
AvPPB
AvIEHL
F Sig.
Levene's Test forEquality of Variances
t df Sig. (2-tailed)Mean
DifferenceStd. ErrorDifference Lower Upper
95% ConfidenceInterval of the
Difference
t-test for Equality of Means
AvPPB - The existence of Peter Principle related behavior patterns
AvIEHL - The existence of incompetent employees in the higher levels of the company hierarchy
According to table above the significant value of the relationships are less than 0.05.
Therefore the relationships are significant. Hence we can conclude that there is a
significant relationship between the structure of the company and the existence of the
Peter Principle effect. The tall organization structures seem to be affected more
according to the results obtained above.
Hence we can conclude the hypothesis statements generated in Chapter 3 as follows.
There is no significant relationship between size of a company and the
existence of Peter Principle effect. Therefore the alternative hypothesis was
rejected with a 95% confidence level.
There is a significant relationship between the structure of a company and the
existence of Peter Principle effect. Organizations with tall structures suffered
more from the Peter Principle effect. Therefore the alternative hypothesis was
accepted with a 95% confidence level.
64
There is a significant negative relationship between the quality of
performance and rewards management practices in a company and the
existence of Peter Principle effect. Therefore the alternative hypothesis was
accepted with a 95% confidence level.
There is a significant negative relationship between the quality of selection
and recruitment practices in a company and the existence of Peter Principle
effect. Therefore the alternative hypothesis was accepted with a 95%
confidence level.
There is no significant relationship between human resource development
practices in a company and the existence of Peter Principle effect. Therefore
the alternative hypothesis was rejected with a 95% confidence level.
4.7 Summary
This chapter presented the data gathered through the empirical study conducted using
employees working in software development organizations in Sri Lanka. Data
collection methods, which were employed for this study, were discussed in detail,
supported by the figures of the collected data. The chapter started with a description
of the general information and demographics of the respondents. It then went on to
check the validity of the data through reliability and validity analysis. Descriptive
statistics were provided for the constructs identified. Inferential analysis techniques
such as correlation analysis and multiple regression analysis were used to analyze the
data set and test the hypotheses. The chapter concluded with the hypothesis test
results.
65
CHAPTER 5 – CONCLUSIONS AND RECCOMENDATIONS
5.1 Introduction
The following sections of the final chapter would discuss conclusions derived from
the data analysis while interpreting data analysis results in detail and the next section
will provide the recommendations. Then, it would analyze the limitations of this
study and highlight the guidelines for the future research.
5.2 Conclusions
The purpose of this research was to identify if the Peter Principle effect exists in
software development firms of Sri Lanka and to analyze the determining factors that
associate with the existence of Peter Principle effect. After identifying the research
problem, three research objectives were formed with five hypotheses. A stratified
random sample size of 396 was derived from the population which consisted of
employees from software development firms of Sri Lanka. A conceptual framework
was designed after in depth literature review. The required data was collected with
the use of an online questionnaire and the data analysis was carried out in an orderly
manner as illustrated in chapter 4.
The first objective of the study was to identify if the Peter Principle effect exists in
the software development companies of Sri Lanka. With the knowledge gained from
the in-depth literature review carried out, the researcher was able to identify the
characteristics that indicate that the Peter Principle effect exists in a company. The
descriptive statistics obtained for the areas that tested whether these characteristics
were present in the software development organizations of Sri Lanka confirmed that
the effect was present in a moderate level. Hence, objective one; was successfully
achieved by proving that the Peter Principle effect is prevalent in the software
development companies of Sri Lanka.
The second objective of the study was to identify and analyze the determining factors
that associated with the existence of the Peter Principle effect. These factors were
66
identified in the literature review and they were studied thoroughly using the survey
data to identify their contribution to the existence of the Peter Principle effect. Five
hypotheses were formed to identify if there was a relationship between the size,
structure, quality of performance and rewards management practices, quality of
selection and recruitment practices and quality of human resource development
practices in an organization and the existence of Peter Principle effect. According to
the results obtained under the data analysis chapter, there are strong significant
relationships between the structure, quality of performance and rewards management
practices, selection and recruitment practices and the existence of Peter Principle
effect. The size of a company did not have a significant relationship with the
existence of the Peter Principle effect. This implies that the Peter Principle effect can
exist in any company regardless of its size. Also the quality of human resource
development practices of a company did not have a significant relationship with the
existence of the Peter Principle effect. This could be due the fact that once an
employee reaches his ultimate level of incompetency, it is hard to improve his
productivity by providing training as he is not suitable for the job. Hence whatever
training that is provided should be provided before an employee reaches his level of
incompetency.The third objective was to propose recommendations to help software
development companies in Sri Lanka to avoid the Peter Principle effect. This has
been achieved by the recommendations provided in the section 5.3 of the
dissertation.At the end of the study the researcher was able to achieve all the
objectives stated in chapter one. Hence we can conclude that the study was
successful.Recommendations and Managerial Implications
Based on the views provided by the industry professionals, findings gathered from
the study and the literature found on the study, this section would propose
recommendations that software development firms can adopt to avoid the Peter
Principle effect.
Opting for flatter hierarchies
As mentioned by Peter and Hull (1969), the Peter Principle is most prevalent in
organizations that have tall hierarchical structures. This was confirmed further by the
67
results obtained from the analysis as the Peter Principle effect was less prevalent in
flatter organizations. Therefore opting for flatter hierarchical structures seems to help
software companies avoid the Peter Principle effect to some extent.
Higher pay without promotions
Employees often accept a promotion, not for the power and prestige, but the
increased pay attached to it. If software companies are willing to offer large pay
increases for excellent work within the same position, the Peter Principle would be
avoided, and the employee could make more money while staying in the position he
enjoys and in which he's competent. It is pretty much easy to carry this concept in
flatter hierarchies.
Clear division of managerial and technical career paths
Software development organizations should offer enough opportunities in
management as well as technical tracks. The skills needed to succeed and measures
of success for each track are very different and sometimes unclear. To succeed in
management track, one needs to be good at dealing with ambiguities, taking
decisions based on partial data, and be able to deal to managing regular management
challenges; measure of success most of the time could be very indirect. It could
mostly be through the success of the team members or the success of a project
assigned etc and hence can be very subjective and debatable. To succeed in the
technical track, one needs to have deep technical and domain expertise, should be
good at solving complex technical problems, and be able to provide technical and
thought leadership; measure of success is very direct and objective and mostly based
on visible results of the individual. Software development firms should identify their
employees’ competencies, guide and groom them to reach their maximum potential
in the right track so that their personal development is serving organizational
development benefiting both parties.
Demotion and Dismissal
68
Although not ethical, perhaps a good way to address the Peter Principle in an
organization would be to institute a policy of demoting employees to their most
appropriate level of work competence. If an employee isn't working out in a higher
position, allowing him to go back to whatever position he excelled in would avoid
the effects of the principle. This would, however, require the supervisor who made
the poor promotion decision to admit he made a mistake, an act not often found in
the higher levels of a hierarchy. Werhane, Radin and Bowie (2003, p.69) stated that,
in dismissing or demoting employees, the employer is not denying rights to persons;
rather, the employer is simply excluding that person's labor from the organization.
This is quite a justifiable reason to demote or dismiss an employee who is not
performing well. From the employer’s side, demotion or dismissal will be reducing
the harm done by that employee to the organization and from the employee’s side he
will get freed from a job that he is neither enjoys nor good at.
Recruiting employees on contract basis
Another solution to the Peter Principle effect is to recruit employees on contract
basis. If the employee doesn’t perform well, the employer has the right to not extend
his contract period and the employee will have to leave the organization once his
contract is over. This will avoid the problems that can arise from demotion and
dismissal as such actions are seen as unethical in the eyes of the employees and it
could harm the good name of the company.
Playing the job role prior to the promotion
Allowing the employee to play the next designation role prior to giving the
promotion will also help avoid the Peter Principle effect. This way the employer will
be able to evaluate whether the employee can do a good job if he was provided the
designation. The promotion can be given based on the performance of the employee
in the given role. If he performs well he will be eligible for the next promotion, if not
he will have to remain in his current position till he performs well enough to carry
out the responsibilities of a designation at the next level.
Improve selection and recruitment practices
69
It is important to always provide proper job descriptions when advertising to avoid
attracting employees who are not suitable for the job. Also companies should ensure
that they have a competent, properly trained selection panel to interview and select
the best candidates. Incompetent interviewers might end up selecting employees who
are far worse than them resulting in an increase in the Peter Principle effect. Also the
organization should have a standardize procedure of recruiting employees.
According to Carpers (2009, p.50), what seem to give the best results are multiple
interviews combined with a startup evaluation period of perhaps six months.
Successful performance during the evaluation period is a requirement for joining the
organization on a full-time regular basis.
Improve performance and rewards management practices
Proper performance and rewards management mechanisms should be put into
practice. Standardized mechanisms like Management by Objectives (MBO),
Balanced Score Cards can be used for proper performance evaluation depending on
the objectives defined by the organization.
According to Gately (1996) employers can avoid the Peter Principle as long as
employees are judged on technical merit and accomplishment, and promotions are
given to the technically proficient and verbally expressive employees, while the less
technically proficient and verbally expressive wait their turn.
Provide proper trainings and improve human resource development practices
According to the analysis done, it was identified that the quality of human resource
development practices in an organization was insignificant when taken as a model
with the other constructs. This could be because when an employee reaches his
maximum level of incompetence, no amount of training can fix his state.
Nevertheless providing proper training while the person is competent might reduce
the chance of him becoming an incompetent employee. Also it will help him identify
his strengths and weaknesses and help him identify what he is most talented in doing.
70
Odiorne (1991) stated, that employees should be taught the skills and tasks in order
to be knowledgeable because ongoing training can prevent competence from eroding
and becoming obsolete.
Outsource performance appraisals
According to “How to get human resources careers” article, Performance appraisals
may be carried out by the company’s human resources department, or it may also be
outsourced. Outsourcing and getting the appraisals done by an independent third
party will help an organization get unbiased and reliable performance evaluations on
their employees.
Recruitment process outsourcing
Recruitment Process Outsourcing (RPO) is an outsourcing arrangement whereby an
external provider takes over part or all of the recruitment functionalities of an
organization. The RPO provider assumes responsibility over the hiring process, from
job profiling through on boarding, as well as the resources, methodologies and
reporting used. With effective implementation RPO can reduce a company’s time to
hire and associated costs, increase the quality of candidate pool and ensure regulatory
compliance. According to Nelson and Gerard (2005), RPO offers companies a
proven way to attract the best talent and, ultimately, ensure the highest possible level
of customer satisfaction.
71
5.3 Limitations of the Study
Every research conducted has limitations of its own nature because of resource
limitations, time considerations and many more. This study also has certain
limitations of its own. Firstly, the research was limited to employees from the
selected organizations. Software organizations can be either product based or project
based. This was not considered when selecting the sample. There is a chance that the
existence of the Peter Principle effect could differ according to the nature of work
carried out. This was not properly captured in the collected sample as it did not have
equal number of respondents from project based and product based software
companies. This was a limitation.
The population of IT professionals in Sri Lanka is high. A proper census could not be
done and only a rough estimate was available regarding the number of IT
professionals. When a convenience based sample is selected to collect responses,
there is a chance that there may be biased results than expected. If a random sample
was selected, the results would have been more accurate.
Data gathering limitations should also be considered. As mentioned in chapter 3 data
was gathered using an online survey. The correctness of gathered data and whether
the respondents provided their actual impressions or manipulative responses was a
major concern.
As mentioned above, there were many limitations in this study and author did his
best to make this study successful with those limitations.
5.4 Directions for Future Research
This study was primarily concerned with identifying whether the Peter Principle
effect existed in software development firms of Sri Lanka. This was successfully
achieved and it was identified that the effect exists. If the limitations identified in the
previous section can be overcome, the study can be further extended and the results
will be more accurate.
72
While studying the factors that determine the existence of the Peter Principle effect
only five factors (the size of the company , the structure of the company, the quality
of performance and rewards management practices, the quality of recruitment and
selection practices and the quality of human resource development practices) were
taken into consideration. Further research should be carried out to identify what other
factors have an influence on the existence of this effect. Further the researcher only
checked whether the there were significant relationships between the factors and the
Peter Principle effect in a broad manner. Each of these factors should be broken
down into smaller and more specific sections and analyzed. For e.g. we could carry
out a thorough search on which kind of performance evaluation mechanisms best
reduce the existence of the Peter Principle effect.
Research could be carried out to identify various human resource development
practices that can help prevent competence from eroding and becoming obsolete.
This way we would be able to identify ways to reduce the Peter Principle effect.
The psychological factors associated with Peter Principle effect can be studied. That
is research should be carried out to identify why employees are eager to accept
promotions even when they know they would be happier doing something else in a
lower level in the hierarchy.
Various organization structures like tall, flat, hybrid etc can be studied to identify the
best organization structure that suits software development firms to minimize the
Peter Principle effect from occurring.
Studies can be carried out on selection and recruitment practices and the skills of an
employee that should be tested to select the best talent so that in the long run, the
company will have less Peter Principled employees.
Problems faced by the HR department when dealing with incompetent employees
should be analyzed to identify proper remedies to minimize the harm done to the
organization by employees who have reached their level of incompetence.
73
Software development firms will have employees working in many areas. It could be
software engineering, quality assurance, business analysis, project management etc.
In each of these areas there could be different reasons as to why the Peter Principle
effect occurs. Studies should be carried out to identify these reasons and to provide
solutions to prevent them.
5.5 Summary
This chapter discussed the interpretation of results obtained from the data analysis
and provided the recommendations based on the findings throughout this study.
Further, it concluded the entire study by exploring pathways for future studies while
explaining the limitations of the completed study.
74
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Appendix A – Questionnaire
Research Questionnaire
A study on worker competence in Software Development Firms of Sri Lanka
Dear Participant,
I am a postgraduate student of University of Moratuwa, currently doing a research
study which is a requisite to complete the Master of Business Administration in
Management of Technology programme.
This questionnaire is designed to study on worker competence in software
development firms of Sri Lanka.
Your responses will be kept strictly confidential and all information provided here
will only be used for this research study.
Thank you very much for spending your valuable time and I greatly appreciate your
help in furthering this research endeavor.
Thanks and Regards,
M.Samaratunga
79
1. Experience in IT industry Years
2. Which of the following category best describes your current job?
Trainees / Associate Level ( E.g. Associate Software Engineers , Associate QA Engineers , Associate Business Analysts etc)
Intermediate Level (E.g. Software Engineers, Business Analysts, QA Engineers etc)
Senior Level (E.g. Senior Software Engineers, Senior QA Engineers, Senior BAs etc)
Lead Level (E.g. Technical Team Leads, Business Consultants etc)
Managerial Level (E.g. Architects, Project Managers etc)
If Other, Please specify: ………………………………………..
3. Experience in the current organization Years
4. How many years have you been employed in your current position? Years
5. How many employees are there in the company that you work at present?
Less than 100 Between 100 and 300
Greater than 300
6. How would you define the structure of your organization in a broad sense?
Flat Hierarchical (Tall)
#From your perspective, in your organization the
occurrence of the following scenarios are:
Ver
y L
ess
Les
s
Mod
erat
e
Hig
h
Ver
y H
igh
7 There are previously competent employees who have become incompetent after getting promoted
80
8 There are employees who are not successful in obtaining further promotions due to incompetence in their current position
9 Some incompetent employees are promoted to a "higher" position to minimize the harm done by them
10 The rate of competent employees leaving the company is
11 Innovative ideas and processes are discouraged as preserving the organization’s existing way of doing work is highly valued than providing efficient service to the customer
12 Highly competent employees are fired to preserve the organization’s hierarchy
13 Some employees are placed very high in the organization’s hierarchy based on personal relationships
14 Incompetent employees get promoted in the hierarchy if they are followers of the superiors
15 Competent people have to push hard to get promoted
16 There are employees who shift the blame on to others for their own actions
17 There are superiors who take credit by getting work assigned to them, done by competent employees under them
18 There are superiors who ignore their duties and focus more on activities that are not direct responsibilities of their position
19 There are employees who are overly stressed , mentally disturbed and frequently sick even though they do not do a lot of productive work
20 The number of incompetent employees in the higher levels of your organization’s hierarchy is
21 The number of competent employees in the lower levels of your organization’s hierarchy is
22 The productivity of the employees in the higher levels of your organization’s hierarchy is
81
23 The productivity of the employees in the lower levels of your organization’s hierarchy is
# With regards to your organization
Str
ongl
y D
isag
ree
Dis
agre
e
Nei
ther
agr
ee n
or
disa
gree
Agr
ee
Str
ongl
y ag
ree
24 I am satisfied with my current job with regards to the responsibilities assigned to me
25 I believe I am eligible for further promotions
26 I am happy to take over more responsibilities
27 My organization has a standard method of evaluating employee performance
28 The employees in my organization are happy with the current performance evaluation method
29 My organization maintains proper job descriptions for all job positions
30 My current job responsibilities have been clearly communicated to me
31 The work that I perform matches with my job description
32 In my organization rewards/promotions are solely based on performance ratings
33 My organization provides comprehensive job descriptions when advertising job vacancies
34 In my organization selection interviews are well structured to identify the best candidates
35 The members of the selection panels in my organization are competent and properly are trained
82
36 My organization gives due attention to skills such as teamwork, leadership, attitude etc when recruiting new employees
37 My organization provides job related training for all employees on technical matters
38 My organization provides training on soft skills (E.g. teamwork, leadership skills etc )
39 My organization has a well defined HR development /talent management process to achieve organizational goals
40 The trainers in my organization are highly competent
41 The employees in my organization are happy with the training they receive
Kindly answer the following questions. The information is required solely for statistical purposes.
42. Age: Years
43. Gender: Male Female
44. Marital Status
Single
Married
45. What is the highest academic qualification you have obtained?
Advanced Level or below
Diploma (E.g. ACS)
Bachelor’s Degree or equivalent professional qualifications (E.g. BCS)
Postgraduate Qualifications
Thank you for filling the questionnaire
83
Appendix B – SPSS Data Analysis Output
This section contains the reliability and validity analysis outputs and the descriptive
statistics outputs obtained via the SPSS software .Please refer Appendix A for the
question descriptions.
Reliability and Validity Analysis
a) Factors that denote the existence of Peter Principle effect
Scale: Reliability Analysis Round 1
Case Processing Summary
N %Cases Valid 396 100.0
Excluded(a) 0 .0Total 396 100.0
a Listwise deletion based on all variables in the procedure.
Reliability Statistics
Cronbach's Alpha N of Items
.929 20
Item Statistics
Mean Std. Deviation NQ7 3.17 1.406 396Q8 3.13 .983 396Q9 2.76 1.221 396Q10 3.55 1.269 396Q11 3.15 1.396 396Q12 2.61 1.269 396Q13 3.16 1.496 396Q14 3.16 1.350 396Q15 3.09 1.038 396Q16 3.30 1.244 396Q17 3.38 1.284 396Q18 3.17 1.311 396Q19 2.96 1.052 396Q20 3.07 1.000 396Q21 3.58 .763 396Q22 3.09 .957 396Q23 3.55 .750 396
84
Q24 2.46 .903 396Q25 2.27 .467 396Q26 2.23 .465 396
Item-Total Statistics
Scale Mean if Item Deleted
Scale Variance if Item Deleted
Corrected Item-Total Correlation
Cronbach's Alpha if Item
DeletedQ7 57.68 180.839 .815 .921Q8 57.72 192.109 .755 .923Q9 58.09 186.019 .785 .922Q10 57.30 188.296 .682 .924Q11 57.70 181.969 .789 .921Q12 58.24 185.746 .760 .922Q13 57.69 178.370 .827 .920Q14 57.69 189.790 .592 .926Q15 57.76 193.956 .644 .925Q16 57.55 184.243 .824 .921Q17 57.47 183.667 .814 .921Q18 57.68 182.817 .821 .921Q19 57.89 191.596 .719 .923Q20 57.78 192.609 .722 .924Q21 57.27 207.169 .268 .931Q22 57.76 194.085 .699 .924Q23 57.30 208.602 .207 .932Q24 58.39 217.622 -.180 .938Q25 58.58 215.535 -.154 .934Q26 58.62 215.577 -.158 .934
Scale Statistics
Mean Variance Std. Deviation N of Items
60.85 213.641 14.616 20
Scale: Reliability Analysis Round 2
Case Processing Summary
N %Cases Valid 396 100.0
Excluded(a) 0 .0
Total 396 100.0
a Listwise deletion based on all variables in the procedure.
Reliability Statistics
85
Cronbach's Alpha N of Items
.956 15
Item Statistics
Mean Std. Deviation NQ7 3.17 1.406 396Q8 3.13 .983 396Q9 2.76 1.221 396Q10 3.55 1.269 396Q11 3.15 1.396 396Q12 2.61 1.269 396Q13 3.16 1.496 396Q14 3.16 1.350 396Q15 3.09 1.038 396Q16 3.30 1.244 396Q17 3.38 1.284 396Q18 3.17 1.311 396Q19 2.96 1.052 396Q20 3.07 1.000 396Q22 3.09 .957 396
Item-Total Statistics
Scale Mean if Item Deleted
Scale Variance if Item Deleted
Corrected Item-Total Correlation
Cronbach's Alpha if Item
DeletedQ7 43.60 177.765 .818 .951Q8 43.64 188.641 .771 .953Q9 44.01 183.106 .782 .952Q10 43.21 184.912 .693 .954Q11 43.61 178.851 .793 .952Q12 44.15 182.620 .764 .953Q13 43.61 175.707 .819 .952Q14 43.60 186.610 .596 .957Q15 43.67 190.692 .651 .955Q16 43.46 181.075 .830 .951Q17 43.38 180.424 .822 .951Q18 43.59 179.635 .827 .951Q19 43.81 188.405 .724 .954Q20 43.69 189.125 .738 .953Q22 43.68 190.548 .717 .954
Scale Statistics
Mean Variance Std. Deviation N of Items
86
46.77 210.418 14.506 15
Principle Component Analysis
Communalities
Initial ExtractionQ7 1.000 .718Q8 1.000 .910Q9 1.000 .665Q10 1.000 .536Q11 1.000 .717Q12 1.000 .664Q13 1.000 .800Q14 1.000 .510Q15 1.000 .483Q16 1.000 .788Q17 1.000 .783Q18 1.000 .794Q19 1.000 .617Q20 1.000 .904Q22 1.000 .865
Extraction Method: Principal Component Analysis.
Total Variance Explained
Component
Initial EigenvaluesExtraction Sums of Squared
LoadingsRotation Sums of Squared
Loadings
Total
% of Varianc
eCumulativ
e % Total
% of Varianc
eCumulativ
e % Total
% of Varianc
eCumulativ
e %1
9.432 62.883 62.8839.43
262.883 62.883
6.550
43.668 43.668
21.321 8.805 71.687
1.321
8.805 71.6874.20
328.019 71.687
3 .759 5.063 76.7504 .589 3.929 80.6795 .485 3.235 83.9146 .480 3.198 87.1137 .390 2.601 89.7148 .315 2.100 91.8159 .274 1.825 93.64010 .247 1.647 95.28811 .210 1.399 96.68612 .183 1.221 97.90713 .136 .909 98.81614 .122 .816 99.63215 .055 .368 100.000
Extraction Method: Principal Component Analysis.
87
Component Matrix(a)
Component
1 2Q7 .846Q8 .814 .498Q9 .813Q10 .732Q11 .822Q12 .795Q13 .843Q14 .645Q15 .695Q16 .853Q17 .845Q18 .852Q19 .759Q20 .781 .542Q22 .765 .528
Extraction Method: Principal Component Analysis.a 2 components extracted.
Rotated Component Matrix(a)
Component
1 2Q7 .704 .471Q8 .885Q9 .689 .436Q10 .581 .445Q11 .781Q12 .745Q13 .855Q14 .630Q15 .546 .430Q16 .831Q17 .835Q18 .839Q19 .731Q20 .900Q22 .880
Extraction Method: Principal Component Analysis.Rotation Method: Varimax with Kaiser Normalization.
a Rotation converged in 3 iterations.
88
Component Transformation Matrix
Component 1 21 .803 .5962 -.596 .803
Extraction Method: Principal Component Analysis.Rotation Method: Varimax with Kaiser Normalization.
b) Factors that explain the quality of performance and rewards management practices in an organization
Case Processing Summary
N %Cases Valid 396 100.0
Excluded(a)
0 .0
Total 396 100.0
a Listwise deletion based on all variables in the procedure.
Reliability Statistics
Cronbach's Alpha N of Items
.915 6
Item Statistics
Mean Std. Deviation NQ27 3.02 1.058 396Q28 2.75 .950 396Q29 2.88 1.051 396Q30 3.06 1.062 396Q31 3.03 1.027 396Q32 2.97 1.030 396
Item-Total Statistics
Scale Mean if Item Deleted
Scale Variance if Item Deleted
Corrected Item-Total Correlation
Cronbach's Alpha if Item
DeletedQ27 14.70 18.779 .751 .901Q28 14.96 19.571 .750 .901Q29 14.83 18.659 .772 .897Q30 14.65 18.318 .806 .892Q31 14.68 19.115 .736 .902Q32 14.74 19.039 .743 .901
Scale Statistics
89
Mean Variance Std. Deviation N of Items
17.71 26.778 5.175 6
Principle Component Analysis
Communalities
Initial ExtractionQ27 1.000 .692Q28 1.000 .688Q29 1.000 .718Q30 1.000 .759Q31 1.000 .670Q32 1.000 .681
Extraction Method: Principal Component Analysis.
Total Variance Explained
Component
Initial Eigenvalues Extraction Sums of Squared Loadings
Total % of Variance Cumulative % Total % of Variance Cumulative %1 4.209 70.144 70.144 4.209 70.144 70.1442 .680 11.331 81.4753 .365 6.090 87.5654 .301 5.024 92.5895 .273 4.557 97.1476 .171 2.853 100.000
Extraction Method: Principal Component Analysis.
Component Matrix(a)
Component
1Q27 .832Q28 .829Q29 .847Q30 .871Q31 .819Q32 .825
Extraction Method: Principal Component Analysis.a 1 components extracted.
90
Rotated Component Matrix(a)a Only one component was extracted. The solution cannot be rotated.
c) Factors that explain the quality of selection and recruitment practices in an organization
Case Processing SummaryN %
Cases Valid 396 100.0
Excluded(a)
0 .0
Total 396 100.0
a Listwise deletion based on all variables in the procedure.
Reliability Statistics
Cronbach's Alpha N of Items
.880 4
Item Statistics
Mean Std. Deviation NQ33 3.07 .966 396Q34 3.02 1.035 396Q35 3.01 1.010 396Q36 3.10 1.018 396
Item-Total Statistics
Scale Mean if Item Deleted
Scale Variance if Item Deleted
Corrected Item-Total Correlation
Cronbach's Alpha if Item
DeletedQ33 9.13 7.393 .688 .866Q34 9.18 6.890 .732 .849Q35 9.18 6.777 .788 .827Q36 9.10 6.877 .754 .841
Scale Statistics
Mean Variance Std. Deviation N of Items
12.19 11.939 3.455 4
91
Principle Component Analysis Communalities
Initial ExtractionQ33 1.000 .673Q34 1.000 .727Q35 1.000 .790Q36 1.000 .754
Extraction Method: Principal Component Analysis.
Total Variance Explained
Component
Initial Eigenvalues Extraction Sums of Squared Loadings
Total % of Variance Cumulative % Total % of Variance Cumulative %1 2.943 73.574 73.574 2.943 73.574 73.5742 .458 11.439 85.0143 .363 9.085 94.0984 .236 5.902 100.000
Extraction Method: Principal Component Analysis.
Component Matrix(a)
Component
1Q33 .820Q34 .852Q35 .889Q36 .868
Extraction Method: Principal Component Analysis.a 1 components extracted.
Rotated Component Matrix(a)
a only one component was extracted. The solution cannot be rotated.
92
d) Factors that explain the quality of human resource development practices in an organization
Case Processing Summary
N %Cases Valid 396 100.0
Excluded(a)
0 .0
Total 396 100.0
a Listwise deletion based on all variables in the procedure.
Reliability Statistics
Cronbach's Alpha N of Items
.924 5
Item Statistics
Mean Std. Deviation NQ37 3.01 1.119 396Q38 2.86 1.090 396Q39 2.72 1.054 396Q40 2.96 1.058 396Q41 2.89 1.016 396
Item-Total Statistics
Scale Mean if Item Deleted
Scale Variance if Item Deleted
Corrected Item-Total Correlation
Cronbach's Alpha if Item
DeletedQ37 11.44 13.806 .815 .903Q38 11.58 14.299 .771 .912Q39 11.72 14.673 .750 .916Q40 11.49 14.245 .811 .904Q41 11.55 14.208 .862 .895
Scale Statistics
Mean Variance Std. Deviation N of Items
14.44 21.842 4.674 5
93
Principle Component Analysis
Communalities
Initial ExtractionQ37 1.000 .787Q38 1.000 .725Q39 1.000 .700Q40 1.000 .784Q41 1.000 .844
Extraction Method: Principal Component Analysis.
Total Variance Explained
Component
Initial Eigenvalues Extraction Sums of Squared Loadings
Total % of Variance Cumulative % Total % of Variance Cumulative %1 3.839 76.789 76.789 3.839 76.789 76.7892 .468 9.363 86.1523 .313 6.256 92.4074 .241 4.829 97.2375 .138 2.763 100.000
Extraction Method: Principal Component Analysis.
Component Matrix(a)
Component
1Q37 .887Q38 .852Q39 .837Q40 .885Q41 .918
Extraction Method: Principal Component Analysis.a 1 components extracted.
Rotated Component Matrix(a)
a Only one component was extracted. The solution cannot be rotated.
94
Descriptive Statistics
Descriptive statistics for the factors that explain the existence of Peter Principle Effect
N Minimum Maximum Mean Std. Deviation VarianceQ7 396 1 5 3.17 1.406 1.977Q8 396 1 5 3.13 .983 .966Q9 396 1 5 2.76 1.221 1.490Q10 396 1 5 3.55 1.269 1.610Q11 396 1 5 3.15 1.396 1.948Q12 396 1 5 2.61 1.269 1.610Q13 396 1 5 3.16 1.496 2.239Q14 396 1 5 3.16 1.350 1.824Q15 396 1 5 3.09 1.038 1.077Q16 396 1 5 3.30 1.244 1.548Q17 396 1 5 3.38 1.284 1.649Q18 396 1 5 3.17 1.311 1.719Q19 396 1 5 2.96 1.052 1.107Q20 396 1 5 3.07 1.000 1.000Q22 396 1 5 3.09 .957 .916
Valid N (listwise) 396
Descriptive statistics for the factors that explained the quality of performance and rewards management practices
N Minimum Maximum Mean Std. Deviation VarianceQ27 396 1 5 3.02 1.058 1.119Q28 396 1 5 2.75 .950 .902Q29 396 1 5 2.88 1.051 1.105Q30 396 1 5 3.06 1.062 1.128Q31 396 1 5 3.03 1.027 1.055Q32 396 1 5 2.97 1.030 1.060
Valid N (listwise) 396
Descriptive statistics for the factors that explained the quality of selection and recruitment practices
N Minimum Maximum Mean Std. Deviation VarianceQ33 396 1 5 3.07 .966 .932Q34 396 1 5 3.02 1.035 1.071Q35 396 1 5 3.01 1.010 1.020Q36 396 1 5 3.10 1.018 1.036
Valid N (listwise) 396
95
Descriptive statistics for the factors that explained the quality of human resource development practices
N Minimum Maximum Mean Std. Deviation VarianceQ37 396 1 5 3.01 1.119 1.253Q38 396 1 5 2.86 1.090 1.188Q39 396 1 5 2.72 1.054 1.111Q40 396 1 5 2.96 1.058 1.120Q41 396 1 5 2.89 1.016 1.032
Valid N (listwise) 396
CorrelationsCorrelations
1 .811** .794** -.721** -.495** .059 -.436**
.000 .000 .000 .000 .240 .000
396 396 396 396 396 396 396
.811** 1 .739** -.691** -.508** .066 -.453**
.000 .000 .000 .000 .188 .000
396 396 396 396 396 396 396
.794** .739** 1 -.587** -.381** .147** -.332**
.000 .000 .000 .000 .003 .000
396 396 396 396 396 396 396
-.721** -.691** -.587** 1 .719** -.019 .564**
.000 .000 .000 .000 .711 .000
396 396 396 396 396 396 396
-.495** -.508** -.381** .719** 1 -.048 .416**
.000 .000 .000 .000 .344 .000
396 396 396 396 396 396 396
.059 .066 .147** -.019 -.048 1 .077
.240 .188 .003 .711 .344 .128
396 396 396 396 396 396 396
-.436** -.453** -.332** .564** .416** .077 1
.000 .000 .000 .000 .000 .128
396 396 396 396 396 396 396
Pearson Correlation
Sig. (2-tailed)
N
Pearson Correlation
Sig. (2-tailed)
N
Pearson Correlation
Sig. (2-tailed)
N
Pearson Correlation
Sig. (2-tailed)
N
Pearson Correlation
Sig. (2-tailed)
N
Pearson Correlation
Sig. (2-tailed)
N
Pearson Correlation
Sig. (2-tailed)
N
AvPRM
AvRP
AvHRD
AvPPB
AvIEHL
Q5
Q6
AvPRM AvRP AvHRD AvPPB AvIEHL Q5 Q6
Correlation is significant at the 0.01 level (2-tailed).**.
96
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